software training courses 2010 corsi di addestramento ... - EnginSoft

The first commercial software to allow Multi-Objective 

Optimization applied to any engineering design area 

SHOW US 

YOUR GREEN SIDE 

International modeFRONTIER Users’ Meeting 2010 goes green... 

‘cause optimize means saving: time, resources, energy. 

And you? Are you ready to go green? 

27 th - 28 th May 2010 - Savoia Excelsior Palace - Trieste - Italy 

To stay competitive and gain market share, companies are forced to continuosly improve the 

quality of the products. While this has been a longtime-held belief for most managers, only in 

recent years has it become clear that achieving higher quality is not necessarily at odds with 

efforts to reduce cost and time-to-market. 

By attending the conference you will get a chance to learn how modeFRONTIER, the leading 

multidisciplinary & multi-objective design optimization tool, is used globally by designers 

and managers in many industries to better understand their product development process, and 

achieve higher quality at reduced cost, allowing them to meet the challenge of producing better 

products faster. 

2010 

Come and attend, we are waiting for you! 

online conference registration 

http://um10.esteco.com 

modeFRONTIER ® is a registered product of ESTECO Srl. International modeFRONTIER Users’ Meeting is an initiative of ESTECO Srl

EnginSoft Flash 

Sometimes challenging times in business 

can leverage, cultivate and grow our 

creativity and innovativeness. They 

remind us on how important networking 

really is to develop and realize new ideas 

and visions and to get inspiration from 

other people and their views. 

In early 2010, we can see a new 

understanding and new beginnings in 

many areas. It is the engineering 

profession that has always created jobs 

and projects, engineers bring things 

forward, they bridge many gaps and 

realize technological advancements. 

Ing. Stefano Odorizzi 

EnginSoft CEO and President 

In the past year, EnginSoft has started several new 

initiatives and strengthened existing ties between our 

customers, partners, the academia, research and industry. 

To us, networking has never been more important. During 

my recent visit to Silicon Valley and the US, I have had 

the opportunity to meet with representatives of BAIA, the 

Business Association Italy America. BAIA is a political 

business network that facilitates the open exchange of 

knowledge and business opportunities. BAIA promotes a 

culture of innovation by fostering entrepreneurial spirit 

and principles, in the US and in Italy. 

This edition of the Newsletter includes a review of my 

encounters with BAIA, the University of California at 

Berkeley, University of Stanford, and the University of 

Santa Clara in October 2009. 

Our readers also hear about SimNumerica, the University 

Spin-Off EnginSoft has co-founded to support the 

development of Digital Mechatronics by using Co- 

Simulation. SimNumerica’s joint expertise is focused on 

environments for the virtual prototyping of mechatronics 

systems based on micro-controllers 

We present the modeFRONTIER 4.1.2 highlights and the 

successful application of the technology at Indesit 

Company that recently has received the 2009 Ecohitech 

Award for its state-of-the-art appliances. Volvo Car 

Newsletter EnginSoft Year 6 n°4 - 3 

Corporation tells us about robust design 

optimization of a bumper system with 

modeFRONTIER. The statistical capabilities of 

the software, this time, are applied to the 

modeling of the spread of a disease like swine 

flu using relatively simple equations. 

For those who are looking for first insights into 

the field of optimization, we recommend the 

article by EnginSoft Germany on optimization 

in today’s product development. 

Further software news feature Magma 5 for 

Process Simulation and Forge 2009. 

Another highlight of this issue is the article on ANSYS 

simulation of carbon fiber and anisotropic materials in the 

ATLAS Experiment and the Large Hadron Collider at CERN. 

We also present R&D News, current research projects, the 

EnginSoft Event Calendar and latest advancements in HPC 

High Performance Computing, as well as our Japan Column 

which features CADdoctor for accelerating reverse 

engineering and an interview with Mr Sakae Morita and Mr 

Kentaro Fukuta of ELYSIUM Co., Ltd. Japan, both speak 

about their time at our International Conference in 

Bergamo this year. 

Akiko Kondoh, EnginSoft’s Consultant in Japan welcomes 

us to Shogatsu, the New Year, with Osechi-ryori and best 

wishes from the land of the rising sun and Monodukuri. 

We hope that you will enjoy reading the many 

contributions of this edition and that some will inspire 

you for 2010. As always, we welcome any feedback and 

ideas for future publications. 

EnginSoft and the editorial team of the Newsletter would 

like to take this opportunity to wish you and your families 

a very Happy and Prosperous New Year! 

Stefano Odorizzi 

Editor in chief

4 - Newsletter EnginSoft Year 6 n°4 

Sommario - Contents 

SOFTWARE UPDATE 

6 modeFRONTIER: release 4.1.2 highlights 

7 MAGMA 5: le nuove frontiere della Simulazione di processo 

10 FORGE 2009 Release notes - dicembre 2009 

14 Elysium’s CADdoctor accelerating Reverse Engineering 

CASE STUDIES 

17 Parametric FEM model optimization for a pyrolitic Indesit oven 

21 Robust Design Optimization of a Bumper System at Volvo Cars using modeFRONTIER 

25 Optimization in product development - An efficient approach to integrate single CAE Technologies up to the 

entire design chain 

29 ANSYS simulation of carbon fiber and anisotropic materials 

32 Aeronautical engines: reduction of emissions and consumptions with a process simulation study 

IN DEPTH STUDIES 

35 Healing the swine flu with modeFRONTIER 

39 New trends in High Performance Computing 

CORPORATE NEWS 

43 Development of Digital Mechatronic Applications using Co-Simulation 

45 About SimNumerica and EnginSoft 

46 Innovation and EnginSoft in the USA 

RESEARCH AND TECHNOLOGY TRANSFER 

48 BENIMPACT Building’s ENvironmental IMPACT evaluator & optimizer 

TRAINING 

50 Continuing Higher Education on CAE: The TCN Consortium 

51 Analizzare cinematica e dinamica dei meccanismi con le tecniche multibody: terminologia, ambiti di 

applicazione ed opportunità 

The EnginSoft Newsletter editions contain references to the following 

products which are trademarks or registered trademarks of their respective 

owners: 

ANSYS, ANSYS Workbench, AUTODYN, CFX, FLUENT and any and all 

ANSYS, Inc. brand, product, service and feature names, logos and slogans are 

registered trademarks or trademarks of ANSYS, Inc. or its subsidiaries in the 

United States or other countries. [ICEM CFD is a trademark used by ANSYS, 

Inc. under license]. (www.ANSYS.com) 

modeFRONTIER is a trademark of ESTECO EnginSoft Tecnologie per 

l’Ottimizzazione srl. (www.esteco.com) 

Flowmaster is a registered trademark of The Flowmaster Group BV in the 

USA and Korea. (www.flowmaster.com) 

MAGMASOFT is a trademark of MAGMA GmbH. (www.magmasoft.com) 

ESAComp is a trademark of Componeering Inc. 

(www.componeering.com) 

Forge and Coldform are trademarks of Transvalor S.A. 

(www.transvalor.com) 

AdvantEdge is a trademark of Third Wave Systems . 

(www.thirdwavesys.com) 

LS-DYNA is a trademark of Livermore Software Technology Corporation. 

(www.lstc.com) 

SCULPTOR is a trademark of Optimal Solutions Software, LLC 

(www.optimalsolutions.us) 

The Diffpack Product Line is developed and marketed by inuTech GmbH 

(www.diffpack.com) 

LINFLOW is entirely a development of ANKER – ZEMER Engineering AB in 

Karlskoga, Sweden. (www.linflow.com) 

The AnyBody Modeling System is developed by AnyBody Technology A/S 

(www.anybodytech.com) 

WAON is a trademark of Cybernet Systems Co.,Ltd Japan 

(www.cybernet.co.jp) 

CADdoctor is a trademark of Elysium Co., Ltd. Japan 

(http://www.elysiuminc.com) 

For more information, please contact the Editorial Team

JAPAN CAE COLUMN 

52 Interview with Mr Sakae Morita, General Manager, 

Marketing and Mr Kentaro Fukuta of ELYSIUM Co., Ltd. 

Japan 

53 New Year Greetings from Japan 

EVENTS 

54 Il mondo della forgiatura a stampi aperti, della 

laminazione piana e circolare, si è dato appuntamento a 

Padova per fare il punto sulle tecniche più avanzate di 

ottimizzazione di processo/prodotto. 

56 Il mondo dello stampaggio a freddo di viterie e minuterie 

metalliche, si è dato appuntamento a Bergamo per fare il 

punto sulle tecniche più avanzate di ottimizzazione di 

processo/prodotto. 

57 Bilancio del Ciclo di Workshop dedicati alla Simulazione 

dei Processi di Deformazione dei Metalli 

58 EnginSoft Event Calendar 

59 EnginSoft 2010 CAE Webinars 

GAMES 

59 Optimization Crossword Puzzle 

PAGE 17 PARAMETRIC 

FEM MODEL OPTIMIZATION FOR 

A PYROLITIC INDESIT OVEN 

PAGE 21 OPTIMIZATION 

OF A BUMPER 

SYSTEM AT VOLVO CARS 

PAGE 32 AERONAUTICAL 

ENGINES: REDUCTION 

OF EMISSIONS AND 

CONSUMPTIONS WITH 

A PROCESS SIMULATION 

STUDY 

Newsletter EnginSoft 

Year 6 n°4 - Winter 2009 

If you want to receive a free copy of the next Enginsoft 

Newsletters, please contact our Marketing office at: 

newsletter@enginsoft.it 

All pictures are protected by copyright. Any reproduction 

of these pictures in any media and by any means is forbidden 

unless written authorization by Enginsoft. 

©Copyright EnginSoft Newsletter. 

Advertisement 

If you want to purchase advertising spaces within our 

newsletter, please contact the Marketing office at: Luisa 

Cunico - newsletter@enginsoft.it 

EnginSoft S.p.A. 

24124 BERGAMO Via Galimberti, 8/D 

Tel. +39 035 368711 • Fax +39 0461 979215 

50127 FIRENZE Via Panciatichi, 40 

Tel. +39 055 4376113 • Fax +39 055 4223544 

35129 PADOVA Via Giambellino, 7 

Tel. +39 49 7705311 • Fax 39 049 7705333 

72023 MESAGNE (BRINDISI) Via A. Murri, 2 - Z.I. 

Tel. +39 0831 730194 • Fax +39 0831 730194 

38123 TRENTO fraz. Mattarello - via della Stazione, 27 

Tel. +39 0461 915391 • Fax +39 0461 979201 

www.enginsoft.it - www.enginsoft.com 

e-mail: info@enginsoft.it 

COMPANY INTERESTS 

ESTECO EnginSoft Tecnologie per l’Ottimizzazione 

34016 TRIESTE Area Science Park • Padriciano 99 

Tel. +39 040 3755548 • Fax +39 040 3755549 

www.esteco.com 

CONSORZIO TCN 

38123 TRENTO Via della Stazione, 27 - fraz. Mattarello 

Tel. +39 0461 915391 • Fax +39 0461 979201 

www.consorziotcn.it 

EnginSoft GmbH - Germany 

EnginSoft UK - United Kingdom 

EnginSoft France - France 

EnginSoft Nordic - Sweden 

Aperio Tecnologia en Ingenieria - Spain 

www.enginsoft.com 

ASSOCIATION INTERESTS 

NAFEMS International 

www.nafems.it 

www.nafems.org 

TechNet Alliance 

www.technet-alliance.com 

RESPONSIBLE DIRECTOR 

Stefano Odorizzi - newsletter@enginsoft.it 

ART DIRECTOR 

Luisa Cunico - newsletter@enginsoft.it 

PRINTING 

Grafiche Dal Piaz - Trento 


The EnginSoft NEWSLETTER is a quarterly 

magazine published by EnginSoft SpA 

Autorizzazione del Tribunale di Trento n° 1353 RS di data 2/4/2008


modeFRONTIER: 

release 4.1.2 highlights 

ESTECO is proud to announce the release of v4.1 of the 

multi-objective optimization and design environment 

software, modeFRONTIER. This state of-the-art PIDO tool, 

written to allow easy coupling to almost any Computer- 

Aided-Engineering (CAE) tool, is now even more powerful 

and user-friendly than previous versions. 

DOE Algorithms 

New features have been added to the list of available 

algorithms in the DOE Sequence: 

Incremental Space Filler 

Inscribed Composite Design 

Uniform Reducer 

Dataset Reducer 

Schedulers and Optimizers 

New features have been added to the list of algorithms 

available in the Scheduler and Optimizers: 

Polynomial Chaos 

Evolution Strategy 

Lipschitz Sampling 

Mixed Integer Programming Sequential Quadratic 

Programming 

Response Surface Algorithms 

Evolutionary Design is now available, which implements a 

symbolic regression technique based on GP (Genetic 

Programming). The algorithm searches for the analytical 

expressions that are able to approximate the training data 

set. The users select the operators to be used among the 

basic mathematical functions (+,-,*,/,cos(),sin(),tg(),exp(), 

etc.) and the program evaluates the analytical expression. 

Data Mining 

New functions have been added to the Tools and Charts 

available in order to make life easier for users when exploring 

and assessing the data available in the Design Space tables: 

Auto-Report 

publishing results is just a few clicks away, thus the users can 

create automatic/custom report according to their needs. 

Principal Component Analysis and Multi-Dimensional 

Scaling 

Principal Component Analysis, designed to extract the 

significant latent variables out of a multi-dimension set of 

data and Multi-Dimensional Scaling, a powerful tool for 

exploring and analyzing sets of data have been added to the 

Multi-Variate Analysis Tool. 

Distribution Fitting 

Distribution Fitting chart has been designed for fitting 

univariate distributions to sets of existing data 

Multi-Vector 

Multi-Vector chart lets the users display vector data in a 

single plot 

The Workflow 

New features have been added to the list of CAD/CAE Nodes 

available in the Workflow library 

Flowmaster V7 

LMS Virtual.Lab 

ANSA 

Design Target Node 

It is now possible to easily assign an external vector as 

target function, by importing from an external text file or 

pasting the data values from the clipboard. This feature, 

coupled with the Levenberg-Marquardt algorithm is ideally 

suited for most of the common curve-fitting design problems. 

For further information: 

Ing. Francesco Franchini - info@enginsoft.it 

Moldflow MPI 

GT-SUITE 

MSC Adams/View


MAGMA 5: le nuove frontiere della 

simulazione di processo 

Con il mese di Novembre 2009 è iniziata la consegna della 

versione 5.0 di MAGMASOFT, denominata MAGMA5, che copre 

i principali aspetti dei processi di colata in SABBIA per 

leghe ferrose e non ferrose. 

MAGMA5 è molto più di una semplice nuova release: è un 

ambiente totalmente nuovo, basato sulle più recenti tecnologie 

software, che rivoluzionerà l'utilizzo della simulazione. 

Con questo nuovo strumento diventa molto più semplice 

creare e gestire i modelli, impostare la simulazione e visualizzare 

in maniera efficiente i risultati. 

Fig.1: Esempi delle diverse modalità di visualizzazione dell’ambiente CAD 

Il nuovo ambiente CAD per la modellazione solida si interfaccia 

con gli altri CAD commerciali, offrendo la possibilità 

di importare ed esportare file geometrici di vari formati: all’interfaccia 

STL si potrà affiancare il formato STEP per un 

periodo di prova di un anno senza costi aggiuntivi, mentre 

diventano disponibili come opzioni attivabili anche le interfacce 

CATIA V5 (solo per le piattaforme Windows) e Pro/E. 

La manipolazione e la visualizzazione dei modelli geometrici 

cambia radicalmente: l’utente può scegliere di 

lavorare con più quadranti attivi fino ad un massimo di 

9; i quattro classici quadranti del preprocessore di 

MAGMASOFT rimangono come una delle possibili soluzioni 

predefinite, anche se l’utilizzatore troverà molto 

comodo e intuitivo disegnare visualizzando il modello 

in un’unica finestra, dove i comandi di rotazione, traslazione, 

zoom e clipping sono utilizzabili in modo dinamico 

e interattivo come nell’attuale post-processore 

(fig1). 

L’albero delle geometrie, disponibile a sinistra dell’ambiente 

CAD, restituisce con immediatezza la visualizzazione 

dei volumi creati o importati. Operazioni quali la 

modifica delle grandezze geometriche o dell’ordine dei 

volumi, la selezione, il copia e incolla di geometrie esistenti 

sono rese semplici e veloci attraverso l’utilizzo del solo 

mouse. 

Il nuovo comando “copy with reference” di volumi creati all’interno 

di MAGMA5 lega le copie al volume originario, in 

modo che ogni modifica effettuata su quest’ultimo sia automaticamente 

applicata a tutte. 

Nuove funzioni CAD sono state implementate per offrire la 

possibilità di modellare più velocemente e in modo flessibile 

modelli complessi: geometrie estruse con sezione termi- 

nale di forma differente rispetto a quella iniziale sono facilmente 

ottenibili con il nuovo comando skin (fig2). 

Inoltre, tutti i solidi possono essere dotati di raggi di raccordo 

semplicemente prevedendoli nel momento in cui si disegna 

la sezione. Per esempio, attraverso la finestra di controllo, 

è possibile impostare le grandezze caratteristiche di 

una sezione trapezoidale: altezza, dimensione della base 

Fig.2: Il comando skin permette di estrudere volumi con sezione finale di forma 

diversa da quella iniziale


Fig.3: Esempio di impostazione di un raggio di raccordo 

Fig.4: Generazione di anime attraverso l’utilizzo di operazioni booleane 

(maggiore e/o minore) e angolo di sformo sono completati 

dall’opzione del raggio di raccordo (fig.3). 

Operazioni booleane sono permesse tra modelli importati da 

CAD esterni e geometrie create all’interno di MAGMA5. Un 

esempio classico è costituito dalla generazione di anime di 

forma complessa con le relative portate (fig.4). 

Al termine della progettazione, il modello CAD deve essere 

tradotto attraverso l’utilizzo della mesh in modello matematico. 

Dalla qualità della mesh dipende l’accuratezza dei risultati 

e il corrispondente tempo di calcolo. MAGMA5 offre 

la possibilità di caratterizzare i volumi necessari 

alla simulazione, attraverso la generazione 

del modello discretizzato, in un numero di 

livelli di affinazione scelto dall’utente 

(fig.5), con il vantaggio di non rinunciare alla 

qualità (visualizzandola istantaneamente 

al termine della generazione) e di minimizzare 

i tempi di calcolo. 

Sui domini riconosciuti dalla mesh vengono 

risolte le equazioni relative alla fluidodinamica, 

alla termica e alle tensioni residue. I corrispettivi 

solutori di calcolo sono arricchiti di 

ulteriori modelli computazionali, come per 

esempio un nuovo modello di turbolenza per 

la simulazione del riempimento della cavità 

che considera l’effetto della tensione superfi- 

Fig.5: Pannello di generazione della mesh 

ciale, mentre un nuovo modello di plasticità viene 

implementato per un calcolo delle tensioni residue 

più accurato, con la possibilità di includere 

l’effetto del contatto del pezzo con le pareti 

della forma di sabbia o anima. 

A completamento della previsione della qualità 

del getto, è possibile simulare qualsiasi trattamento 

termico. Per esempio, considerando il 

classico trattamento T6, il modello di calcolo degli 

stress residui valuterà il rilassamento delle 

tensioni dopo colata durante la fase di solubilizzazione, 

l’insorgere di nuove eventuali tensioni 

residue durante la fase di tempra e il successivo 

stato di tensione generato dalla fase di invecchiamento. 

Dal momento che le operazioni meccaniche 

(come per es. la smaterozzatura) mutano 

ulteriormente la distribuzione delle tensioni residue 

presenti a seguito della colata, MAGMA5 of- 

fre la possibilità di includerle nell’analisi di stress. La conoscenza 

delle tensioni residue permette al progettista di valutare 

più accuratamente la resistenza in esercizio di un 

componente e qualora fosse di interesse, attraverso l’utilizzo 

di MAGMAlink, di utilizzare la loro distribuzione come 

stato iniziale in una simulazione strutturale. 

L'impostazione del processo di simulazione può ora essere 

elaborato in finestre parallele agli ambiente CAD, mesh e 

postprocessore. Nella finestra principale coesistono, infatti, 

diversi menù a tendina nei quali è possibile entrare con un 

semplice “clic” per poter creare la geometria (fig.6a), visua-

Fig.6: Pannello di gestione: a) ambiente CAD,b) visualizzatore della mesh,c) settaggio della simulazione. 

Fig.7: Previsione della qualità del getto attraverso la visualizzazione di 

differenti risultati contemporaneamente. 

lizzare la mesh, definire i parametri di processo (fig.6b) e 

analizzare i risultati (fig.7.). Il veloce passaggio da una finestra 

all’altra comporta un notevole miglioramento nei 

tempi di impostazione della simulazione. 

L’analisi dei risultati viene agevolata dalla visualizzazione di 

più finestre contemporaneamente, in ognuna delle quali è 

possibile analizzare un risultato diverso e usufruire dei classici 

comandi di rotazione dinamica, traslazione, sezione e 

animazione (fig.7). 

L’innovativo comando “picking” permette di rilevare il valore 

puntuale di qualsiasi risultato con un semplice “clic” nella 

zona di interesse. Una finestra informativa restituisce le 

coordinate del punto selezionato e 

il corrispondente valore. Inoltre è 

possibile salvare in formato grafico 

l’evoluzione dei valori dei risultati 

fluidodinamici e termici dei punti 

selezionati per l’intero arco di tempo 

simulato (fig.8). 

I settaggi, predisposti dall’utente 

(viste, sezioni, scale e risultati), per 

il salvataggio delle immagini dei risultati 

vengono memorizzati nel file 

MAGMASOFT.pdb che consente di richiamare 

le stesse impostazioni anche 

per le versioni successive, agevolando 

l’analisi di confronto di di- 


verse simulazioni e di salvare le 

immagini in background. 

La nuova modalità di visualizzazione 

e i nuovi criteri (microporosità 

e proprietà meccaniche) del 

modulo opzionale Non-ferrous 

permettono metodi di verifica 

delle prestazioni del getto più efficienti 

e immediati. 

Tale modulo restituisce, per le leghe 

di alluminio, la previsione 

della microstruttura e delle proprietà 

meccaniche allo stato “as 

cast”, calcolate sulla base della composizione chimica della 

lega, della velocità di raffreddamento del sistema e dei trattamenti 

della lega eseguiti prima della colata (es degasaggio). 

I moduli MAGMAhpdc, MAGMAlpdc e MAGMAPermanent 

mold, oltre MAGMAlink e MAGMAdisa, sono in fase di completamento 

e verranno rilasciati con la versione 5.1, mentre 

MAGMAfrontier con la successiva 5.2. 

Nel periodo di transizione, MAGMA4.4. e MAGMA5 potranno 

coesistere sullo stesso hardware, a condizione che il sistema 

operativo sia supportato per entrambe le versioni. 

MAGMA5 è stato sviluppato in linguaggio JAVA per sfruttare 

al meglio le potenzialità di Windows 64 bit, mentre rimangono 

supportate le piattaforme LINUX RedHat5 e SU- 

SE11 a 64 bit. 

Per prendere visione degli hardware suggeriti e delle piattaforme 

supportate dal nuovo software visitate la pagina: 

http://www.enginsoft.it/software/magmasoft/news/ 

magma5.html 

Le date dei corsi di formazione sono come di consueto pubblicate 

alla pagina: 

http://www.enginsoft.it/formazione/corsi2010/ 

processo/proc14.html 

Per maggiori informazioni: 

Ing. Nicola Gramegna - info@enginsoft.it 

Fig.8: Visualizzazione di valori puntuali e della corrispondente curva temperatura-tempo


FORGE 2009 

Release notes - dicembre 2009 

Nel mese di dicembre 2009 è stato rilasciato da Transvalor il 

nuovo pacchetto di simulazione Forge 2009®, lo strumento 

ideale per la simulazione dell’intero processo di stampaggio 

a caldo o a freddo dei più svariati componenti (alberi, giunti, 

ingranaggi, flange, raccordi, cuscinetti, bulloni, viti, fasteners, 

…). È possibile simulare la sequenza completa di un 

processo di forgiatura multistadio con una cinematica degli 

stampi anche molto complessa (stampi flottanti o pre-caricati), 

seguita da raffreddamenti, tranciatura bave e/o trattamenti 

termici. 

Forge 2009® è la logica evoluzione di Forge2008® ed è un 

software di simulazione FEM dedicato alla simulazione di processi 

assialsimmetrici (2D) e di qualsivoglia geometria (3D), 

che è stato sviluppato seguendo le indicazioni degli utilizzatori. 

Forge 2009 – ottimizzazione dei processi di forgiatura 

La principale novità introdotta nella nuova release è la possibilità 

di effettuare una procedura automatica di ottimizzazione 

per un determinato progetto in una o più operazioni. 

Già nelle versioni precedenti era stato introdotto il concetto 

di “chaining”, che 

consentiva di impostare 

una intera sequenza 

di stampaggio 

e concatenare le singole 

operazioni in un 

unico calcolo, trasferendo 

in automatico i 

risultati tra le stazioni. 

Oggi è possibile 

definire delle variabili 

in ingresso sulla prima 

operazione, come 

ad esempio le dimensioni 

caratteristiche 

della billetta o altri 

parametri quali per esempio la corsa della pressa, chiedendo 

al software di ricavare i migliori risultati per degli obiettivi 

definiti dall’utente, come per esempio il migliore riempimento 

delle impronte nell’ultima operazione o la richiesta 

di un pezzo privo di ripieghe o ancora la 

minimizzazione del carico pressa. Il modulo di ottimizzazione 

effettua una serie di “run”, valutandone 

i risultati e modificando le variabili in ingresso, 

in modo da ottenere i migliori risultati 

possibili. Le varie configurazioni sono classificate 

in funzione della combinazione di obiettivi raggiunti, 

consentendo di individuare le configurazioni 

migliori. 

Interfaccia di ottimizzazione 

Il progettista, che in precedenza testava con Forge solo un 

numero limitato di ipotesi, può limitarsi ora a definire, mediante 

una interfaccia user-friendly, le variabili, i vincoli del 

processo e gli obiettivi da raggiungere, lasciando a Forge il 

compito di esplorare un numero decisamente maggiore di 

configurazioni: le migliori possono essere magari ipotesi che 

il progettista non avrebbe considerato. Grazie all’esperienza 

maturata utilizzando questo strumento, Transvalor, l’azienda 

che sviluppa il software, 

intende aggiungere 

altre variabili ed obiettivi 

che possono essere 

gestiti dall’utente nelle versioni successive. 

In caso fosse necessario utilizzare uno strumento più flessibile 

ed in grado di consentire un’analisi più accurata dei risultati, 

è possibile interfacciare il software con il software 

modeFRONTIER prodotto da ESTECO e distribuito da 

EnginSoft; è possibile in questo caso sfruttare i nodi diretti 

verso i principali CAD e modificare le geometrie di pezzo o 

stampi, importandole quindi in Forge, per lanciare poi il calcolo 

ed utilizzare gli strumenti avanzati del programma per 

l’analisi degli obiettivi. 

Il processo di laminazione circolare – ring rolling 

L’esperienza accumulata grazie ai diversi utilizzatori del software 

per il processo di ring-rolling ha consentito a Transvalor 

di introdurre una serie di migliorie al modello utilizzato, che 

hanno portato ad un deciso miglioramento della qualità dei 

risultati, con una riduzione dei tempi di calcolo nell’ordine 

del 30% rispetto alla versione precedente. Tra le novità più 

significative, la possibilità di inserire la curva di laminazione 

che normalmente viene impostata dall’operatore del laminatoio, 

ed ottenere in automatico le curve di movimento di coni 

e mandrino per Forge. Per il mandrino, la velocità può anche 

essere modulata in funzione della crescita del diametro 

esterno dell’anello. Il nuovo modello consente ora anche di 

rilevare la velocità di rotazione del mandrino folle per effetto 

del contatto con il pezzo. 

Molto lavoro è stato dedicato al miglioramento delle routine 

di calcolo: i nuovi algoritmi PETSC consentono di risolvere 

profili anche molto complessi in tempi 

molto inferiori ai precedenti, con una 

precisione di risultati decisamente 

maggiore grazie all’introduzione di nuove 

funzioni di contatto. 

Simulazione processo di Ring-rolling 

Il processo di fucinatura – 

nuovi strumenti disponibili 

Il processo di forgiatura/fucinatura è 

caratterizzato da un numero molto ele-

vato di passate, ognuna con diversi colpi ed una movimentazione 

del pezzo anche complessa con dei tempi di attesa tra 

ogni colpo/passata. Il modello precedente, presente in 

Forge2008, è stato ulteriormente arricchito di funzioni, tra le 

quali i tempi morti tra due colpi consecutivi, nel quali viene 

calcolato il raffreddamento del pezzo, l’arresto del calcolo 

una volta che il pezzo è uscito dagli stampi, una migliore gestione 

degli scorrimenti del pezzo rispetto agli stampi grazie 

all’uso di manipolatori. Dal punto di vista operativo, la miglioria 

principale è una modifica e semplificazione delle modalità 

di definizione delle passate, attraverso un nuovo formato 

di file generato in automatico dal programma. 

Le lavorazioni di fucinatura hanno l’obiettivo di chiudere le 

porosità, che sono causate dal processo di colata del lingotto 

e che hanno una notevole influenza sulla qualità del pezzo 

finito. Transvalor ha dedicato molte energie per implementare 

questo aspetto in Forge: è stata aggiunta la possibilità 

di definire sul lingotto una distribuzione iniziale di porosità 

e tra i risultati la possibilità di visualizzare la chiusura 

di tali porosità. La distribuzione iniziale di porosità può 

essere ottenuta anche mediante l’uso di un altro software di 

Transvalor, Thercast, dedicato alla simulazione del processo 

di colata e raffreddamento in lingottiera ed in grado di calcolare 

la formazione di porosità con il criterio di Yamanaka. 

I risultati calcolati da Thercast possono essere trasferiti direttamente 

in Forge, per ottenere una distribuzione molto 

realistica delle porosità nel lingotto iniziale. 

Calcolo delle porosità in Thercast e trasferimento in Forge 

Altro aspetto fondamentale in questo tipo di processi è l’evoluzione 

del grano cristallino funzione della ricristallizzazione. 

Forge da questa versione è in grado di seguire l’evoluzione 

del grano cristallino per effetto della ricristallizzazione statica 

e dinamica, basandosi sulle definizioni dei materiali provenienti 

da prove sperimentali e dal software JmatPro: sono 

disponibili i dati di alcune leghe molto particolari e critiche 

per questi aspetti, quali: acciaio AISI316L, Inconel 718, 

Waspalloy ed alcuni acciai al manganese. 

Stampaggio lamiere - anisotropia 

Nel campo dello stampaggio ed imbutitura delle lamiere gli 

effetti legati all’anisotropia del materiale sono rilevanti. 


Nella nuova versione di Forge è stato introdotto un nuovo 

modello di materiale nel quale è possibile specificare i parametri 

di anisotropia secondo il modello di Hill. Il solutore è 

stato quindi adeguato per tener conto di questa nuova definizione 

e nel post-processore sono stati aggiunti dei risultati 

in grado di consentire una migliore comprensione di questi 

effetti 

Contatto materiale-materiale e ripieghe 

Grazie alle esperienze provenienti dagli utilizzatori, soprattutto 

nel campo dello stampaggio dei materiali non ferrosi 

(ottone ed alluminio), si è evidenziata la necessità di ripensare 

il modo nel quale il software evidenzia la formazione e 

l’evoluzione delle ripieghe. Sono state quindi messe a punto 

delle nuove funzioni di contatto in grado di gestire in maniera 

più efficiente le situazioni, ove il materiale ripiega su se 

stesso. Contemporaneamente è stato sviluppato un nuovo 

approccio per la visualizzazione dei difetti nel post-processore: 

quando due lembi di materiale vengono in contatto tra loro, 

si genera un tracciante, il cui movimento nel resto della 

corsa di stampaggio consente di valutare con una notevole 

precisione forma e dimensioni delle ripieghe. Oltre alla localizzazione 

delle ripieghe, che era già presente nella precedente 

versione, il progettista è in grado di comprendere se, effettivamente, 

il difetto interessa 

il pezzo e per che spessore 

o se esce verso le bave e 

quindi non è critico per la qualità 

del pezzo. Effetti indotti 

di questi miglioramenti al motore 

di calcolo sono stati una 

riduzione dei tempi di calcolo 

stimabile mediamente dal 20% 

al 30% a seconda del numero 

di nodi utilizzato e del tipo di 

calcolo impostati, miglioramento 

riscontrato sia sulle configurazioni singolo processore, 

che sulle più potenti piattaforme cluster. 

Tracciatura delle ripieghe 

Un nuovo “wizard” per lo stampaggio a freddo 

Nella versione 2008 è stato introdotto il concetto di “wizard”, 

uno strumento in grado di guidare passo-passo l’utente 

nella creazione della singola operazione, utile soprattutto 

per i neofiti, che possono creare con pochi parametri un progetto 

pronto per essere risolto. Nella versione 2009 è stato 

aggiunto un wizard per lo stampaggio a freddo. 

Molte le migliorie introdotte nel pre- e nel 

post-processing 

Per Transvalor le linee di sviluppo del software sono sempre 

guidate dai suggerimenti degli utenti. Nella nuova versione 

diverse sono le migliorie apportate, che riassumiamo di seguito. 

1. Pre-processore e template di processo 

Diverse migliorie minori molto utili sono state introdotte nelle 

finestre di impostazione dei progetti. Nel pre-processore


l’attenzione si è concentrata, in particolar modo, sul miglioramento 

di alcuni template di processo, i modelli che servono 

da base per l’impostazione di tipologie particolari di calcolo. 

Per quanto riguarda il modello delle presse ad energia, pressa 

a vite e maglio, è stato riformulato invece il modello in 

grado di tener conto dell’efficienza della macchina al procedere 

dei numero di colpi ed è stata aggiunta la possibilità di 

inserire un tempo di pausa prima dell’inizio dello stampaggio, 

con il risultato che ora le temperature del pezzo all’inizio 

del processo sono molto più precise. 

Per quanto riguarda lo stampaggio di ottone, nelle configurazioni 

di stampaggio a forare, ora è possibile introdurre carrelli 

inclinati, seguire lo stampaggio di più particolari (multi 

impronta), valutare con precisione i carichi su ogni punzone 

in funzione della resistenza del cuscino. Sono in corso modifiche 

ancora più rilevanti per questo modello, con la possibilità 

di gestire configurazioni a forare più complesse o a cam- 

Stampaggio ottone con carrelli inclinati Bigorniatura anello in acciaio 

pana. Sempre in tema di cinematiche molto complesse, sono 

stati messi a punto nuovi modelli di stampi flottanti in traslazione 

e rotazione, ma anche di stampi “slave” sia in traslazione, 

che in rotazione, collegabili al movimento di altri 

stampi “master”. 

Per quanto riguarda la laminazione, sono stati sviluppati nuovi 

strumenti in grado di creare, per rivoluzione, il profilo dei 

rulli a partire da un profilo 2D, la cui forma può essere modificata 

direttamente nel pre-processor, muovendo o trascinando 

in nodi del profilo. 

Parlando poi delle funzioni comuni a tutti i progetti, è proseguito 

il miglioramento delle funzioni di meshatura da geometrie 

STL, con una qualità decisamente superiore rispetto 

alle versioni precedenti. 

2. Solutore 

L’evoluzione della parte del software relativa al calcolo ha seguito 

due filoni principali. Le routine di calcolo sono state 

sensibilmente migliorate, ottenendo una migliore qualità della 

mesh, in grado di rispettare meglio la forma degli stampi, 

una maggiore stabilità del solutore soprattutto per configurazioni 

multi-processore e/o multi-core e, di conseguenza, 

tempi di calcolo significativamente minori (-20-30% a seconda 

dei casi) rispetto alla versione precedente. Il solutore è 

stato inoltre modificato per tener conto degli effetti di ani- 

sotropia del materiale e tutta una serie di nuove opzioni impostabili 

nei modelli dedicati ai singoli campi di applicazione: 

per esempio i raffreddamenti prima dello stampaggio nel 

modello della pressa a vite, nuove funzioni PETSC per la laminazione 

circolare, nuove funzioni per il tracciamento delle 

ripieghe. Per quanto riguarda il secondo aspetto, l’interfaccia 

per il lancio dei calcoli è stata ulteriormente evoluta e si presenta 

ora con delle nuove funzioni e scorciatoie per le operazioni 

più comuni. 

3. Post-processore 

Lo sviluppo del post-processore, funzione delle richieste degli 

utilizzatori, ha riguardato diversi aspetti. Tra i più utili in 

evidenza la creazione di un cubo di navigazione, che rende 

immediata la rotazione del modello nelle viste ortogonali agli 

assi principali. Sempre nella direzione di una migliore gestione 

del punto di vista scelto, è stata implementata la possibilità 

di salvare il “workspace”: l’utente carica i risultati di 

interesse (scalari, vettoriali, plot) anche 

per più progetti da confrontare, 

sceglie il punto di vista e le opzioni 

grafiche, carica eventuali animazioni e 

salva il “workspace”. Caricando questo 

file, vengono quindi ripristinate tutte 

le scelte dell’utente, opzione che consente 

un notevole risparmio di tempo 

nella fase di analisi dei risultati. 

La vista dei soli risultati in superficie 

non consente una valutazione di quanto 

realmente succede all’interno del 

pezzo: per questo scopo si utilizzano 

dei piani di sezione. Tra le nuove funzionalità 

introdotte per questo strumento, le più significative 

sono la possibilità di muovere il piano attorno ad un asse, 

la possibilità di selezionare dei punti sul piano, rilevandone 

i valori calcolati, e la possibilità di ottenere un grafico 

dell’area del piano in funzione della corsa impostata. Sempre 

per questo strumento risulta utile la possibilità di esportare 

il profilo del piano in formato dxf ed in coordinate XY, che 

può quindi essere utilizzato in qualsiasi CAD, ma anche la 

possibilità di salvare, in un determinato istante della corsa, 

una animazione che mostri il piano di taglio che scorre attraverso 

il pezzo in una determinata direzione, o secondo una 

rotazione attorno ad un asse. È così possibile valutare in una 

unica animazione cosa accade nelle varie sezioni del pezzo. 

Sempre in termini di strumenti di interfaccia con strumenti 

CAD o FEM, da ricordare 

la possibilità 

di esportare in 

formato .STL, scegliendo 

quali oggetti 

esportare e la 

possibilità di generare 

un file .UNV (Ideas 

universal file), 

che contiene sia la 

forma, ma anche 

Albero - Calcolo accoppiato tensione sugli stampi

tutti i risultati calcolati: è possibile quindi trasferire ad un 

altro strumento FEM quanto calcolato in Forge, per effettuare 

altri tipi di analisi, ad esempio del pezzo nelle condizioni 

di carico corrispondenti alla sua messa in opera. 

Degno di nota è inoltre il miglioramento dell’interfaccia di 

esportazione .vtf, che consente di esportare l’animazione di 

un risultato in una forma ove l’utente ha la possibilità di 

cambiare il punto di vista e/o lo zoom. Con il nuovo visualizzatore 

GlView Express, scaricabile gratuitamente, è ora 

possibile visualizzare nello stesso file più risultati, rendendo 

decisamente più agevole la comunicazione delle informazioni 

tra colleghi o verso l’esterno. 

Miglioramento continuo del database dei materiali 

Il database dei materiali è sempre stato uno dei punti cardine 

di Forge, con le curve di deformazione a caldo ed a freddo, 

le caratteristiche elastiche e le proprietà termiche di oltre 

800 leghe ferrose e non ferrose. In questa versione sono 

stati aggiunti una serie 

di materiali provenienti 

dal programma 

JmatPro, quali acciai 

al Boro, micro legati, 

acciai inox, superleghe 

(inconel718, nimonic, 

waspalloy), 

leghe di Titanio, per i 

quali sono state calcolate 

le curve reologiche 

e le caratteristiche 

fisiche da temperatura 

ambiente alle 

temperature di 

stampaggio a caldo. 

Da evidenziare come 

siano stati introdotti 

anche dei materiali 

che tengono conto 

dell’evoluzione del grano cristallino causato dalla ricristallizzazione 

e del kinematic hardening. 

Simulazione stampaggio a caldo fuso a snodo 

Simulazione laminazione di prodotti lunghi 

Installazione – versioni disponibili 

Già nella versione precedente era possibile impostare una architettura 

client-server, concentrando le operazioni di calcolo 

sulla macchina più potente e demandando alle macchine 

client la preparazione dei calcoli e l’analisi dei risultati. Il sistema 

è stato ulteriormente evoluto, aggiungendo la possibilità 

di una licenza “floating”, che può essere attivata a turno 

su diverse macchine, aumentando la flessibilità di utilizzo 

in ambiente multiutente.La gamma di possibili installazioni 

di Forge è stata ampliata rispetto alla versione precedente. 

Oggi è possibile installare il software sia in sistema operativo 

a 32 o 64 bit Windows XP, Server® 2003, Server® 2008, 

VISTA business, Linux Red Hat Enterprise o SLES 10 64bits. In 

termini di piattaforme hardware, Forge sfrutta appieno la parallelizzazione 

del calcolo, quindi è possibile utilizzare una 

macchina con singolo processore 1-4core, con più processori 


o sistemi cluster fino a 32 core, le cui code possono essere 

gestite anche mediante i software pbs v5, pbs v9, lsf e sge. 

Gli ultimi benchmark effettuati su piattaforme equipaggiate 

con i nuovi processori Nehalem i7 (serie XEON 55**), con due 

processori 4core, hanno mostrato una notevole efficienza, 

con tempi di calcolo paragonabili a quelli prima ottenibili solo 

con un cluster, con una semplicità di gestione decisamente 

maggiore. Questo rende ora possibile lanciare anche su 

queste piattaforme analisi molto pesanti quali la laminazione 

di anelli, mesh molto fini o analisi con molti incrementi. 

Conclusioni 

Si può quindi affermare che Forge 2009® è un programma 

sempre in costante miglioramento, che ha raggiunto una notevole 

semplicità d’uso grazie all’esperienza 

accumulata con le versioni 

precedenti e i suggerimenti 

provenienti dagli utenti. Molte delle 

novità introdotte portano la versio- 

Stampaggio a freddo 

ne 2009 ad un livello di 

precisione ed accuratezza 

decisamente superiore alla 

versione precedente. 

Dall’altra parte, la maturità 

raggiunta dal prodotto 

consente sempre un 

facile e rapido inserimento in qualsiasi ambiente tecnico, per 

la progettazione di prodotti ottenuti per stampaggio e l’ottimizzazione 

dei relativi processi produttivi. Con Forge 2009 è 

quindi possibile migliorare rapidamente la qualità dei pezzi, 

ridurre gli sprechi di materiale e aumentare la durata degli 

stampi e delle macchine di stampaggio. È possibile inoltre 

valutare in modo anticipato senza sorprese la stampabilità di 

nuove forme o di materiali poco conosciuti. 

EnginSoft, distributore in Italia del software Forge, grazie a 

tecnici specializzati con oltre 10 anni di esperienza, offre alle 

aziende del settore, formazione del personale ed avviamento 

all’uso oltre al supporto nell’installazione, nonché attività 

di simulazione su commessa, con impostazione del caso, analisi 

dei risultati e consulenza sull’ottimizzazione del processo. 

Rollatura del filetto 

Calcolo accoppiato vite, 

bullone e lamiera 


Ing. Marcello Gabrielli - Responsabile di prodotto FORGE 

info@enginsoft.it


Elysium’s CADdoctor accelerating 

Reverse Engineering 

1. 3D data utilization in Reverse Engineering 

As the noncontact 3D measuring machine has become so 

popular, the digitalization of physical models is needed more 

than ever. Traditionally, the main purpose of measuring physical 

models was ”Inspection” to determine if the products were 

manufactured following the original design by comparing CAD 

data and point cloud data measured by the contact measuring 

machine. However applications in “Reverse Engineering” have 

recently attracted a lot of attention. Typically, what we mean 

here is the way how to use CAD data produced from the huge 

Figure1: Deformation to the nearest point (solid arrow) and the ideal deformation orientation 

considering feature line (dashed arrow) 

number of point cloud and polygon data measured by the 

noncontact measuring machine. The objective of creating a CAD 

model from the point cloud and polygon data is either related to 

design purposes or to simulation purposes. 

Design purposes 

Design: Creating CAD models from clay models and using 

them for the design 

Digitalization of CAD models from own products: Creating 

CAD models only from physical models and using 

them for the design 

Mold building: Measuring existing die to produce 

the second die 

Simulation purposes 

Benchmark: Simulation of own products and 

products developed by other companies 

Simulation: e.g. Creating a CAD model of a golf 

bag for the design of the luggage space 

The key factor for effective Reverse Engineering is 

the creation of a CAD model from measured point cloud and 

polygon data. Generally, when a CAD model has been created for 

reverse engineering, the measured polygon data has been 

divided into areas and a Brep surface was created for each area. 

In this process, it is important to translate the right CAD model 

for the purposes as the required CAD quality depends on the 

application of the translated CAD model. For example, if the 

purpose is design, high quality CAD data of class A representing 

exact feature lines and curved surfaces are required as they will 

be used for design and manufacturing later. However if the 

purpose is simulation, the required quality is different. Indeed, 

very often, it is not necessary to have the same quality as for 

design. The created CAD model is meshed by using CAE and in 

many cases, moderate quality is sufficient. (*) 

(*) When we consider CAE simulation, there is a tendency to 

think that measured polygon data can be directly used for 

meshing and there is no need to create a CAD model. However, 

such polygon data is not sufficient for meshing and often leads 

to low accuracy simulation results because of the noise involved. 

It is also a problem to increase the number of mesh elements. 

Hence the translated CAD model is usually used for 

meshing before CAE simulation instead of using the 

measured polygon data directly. 

Reverse Engineering performances have improved 

through the years, but the huge amount of manhours 

to create CAD models is still a relevant matter, 

in both areas of design and simulation. In fact, there 

is no solution to create high quality CAD data 

automatically and efficiently enough for design 

purposes. In recent years, another approach has 

evolved, which consists in using original CAD data and 

transforming it to fit with the polygon data, in order to produce 

the final CAD data of measured polygon data. However, this is 

certainly not the best solution. When considering this approach, 

it would be better to transform the model by fitting the feature 

lines of the original CAD data to feature the lines of the polygon 

data. However, the current process is to use commercial software 

which only transforms by fitting the original shape to the 

nearest polygon. (See Figure 1). 

Figure 2: Measured polygon data (left) and CAD data from polygon (right) 

The complicated surface including the internal opening section can be created automatically. 

For simulation purposes, usually the required CAD quality is not 

as high as for design purposes. Though, it would be much 

appreciated to represent the position of the feature line to 

define the right boundary condition. The reduction of the 

number of surfaces of the CAD model is also important for the 

effective mesh generation, although it is not easy because the 

CAD model has lots of square surfaces created from polygon 

data. Besides, it is also important that the translated CAD model 

can be used in CAE. To solve these problems, the latest version 

of CADdoctor has the fully-automated capability to create CAD

model data from polygon data by reproducing the feature line 

position, which had to be done manually before and it was very 

time-consuming. Using this capability, CADdoctor represents an 

extremely complex surface including an internal opening section 

as a face. Hence it also has the ability to produce the best model 

for the simulation. (See Figure 2) 

2. Reverse Engineering capability in CADdoctor 

2-1 Automatic CAD data generation from point cloud and 

polygon mesh 

Point cloud data and polygon data measured by a 3D measuring 

machine representing physical products (prototype or 

commercial product) can be translated into 3D CAD data 

generating NURBS surfaces automatically. Prior to creating CAD 

data from polygon data, the surface is segmented. For the 

segmentation, the geometry of the polygon data is captured, 

Figure 3: The original CAD data is copied to polygon (left); copied parting line (right) 

fillets are automatically detected, and the range of each 

segment is determined automatically to approximate the surface 

composed in the CAD model. Planar, cylindrical, and conic 

surfaces for analysis representation are recognized automatically 

allowing a segmentation based on the surface type. If 

prototyping in-house design and original CAD data are at hand, 

the edge from the original CAD data can be copied to polygon 

and used as parting line for segmentation. (See Figure 3) 

Segmentation is automatic, but there are cases where the 

segmentation may not be adequate due to unevenness occurring 

from noise. Such areas can be edited to adequate segmentation 

by using the editing commands, such as part, merge and extend. 

Once the segmentation is complete, by clicking a button, NURBS 

surfacing will complete on each segment and complete CAD data 

is automatically created. With CADdoctor, surfacing is also 

possible on a trim surface surrounded with complex edges, 

allowing creation of effective CAD data with a simple surface 

based on segmentation. Moreover, after the batch surface 

generation, minor amendments, if required, can be 

made without re-executing the process, since partial 

segments can be repaired or surface types can be 

switched. Time spent on repair is reduced. 

The advantage of the CADdoctor Reverse Engineering 

capability is that manual operation and data healing 

are at minimum and creating 3D data can be done 

nearly fully automatically. From Elysium’s 

independent study, CADdoctor creates 3D CAD data 

from polygon data in 1/10 to 1/30 of the time 

needed for using other translation products. 


2-2 Fitting original CAD data to polygon data 

The product design in 3D CAD data can be transformed to be 

consistent with the polygon data taken from an actual product 

using a 3D measuring machine. This is the powerful advantage 

of CADdoctor. This capability can be applied not only to the 

polygon data, but also to the polygon from CAE. (See Figure 4) 

When deforming actual CAD data, the distance between the CAD 

data and the polygon data is recognized, however, polygon data 

from a measuring machine has a subtle difference from the 

actual geometry due to noises. By setting tolerances for the 

differences to avoid this impact, the target for deformation will 

be faced larger than the tolerance, and faces smaller than the 

tolerance will be excluded from target. 

The target face for deformation is consistently deformed with 

the polygon. However, if the face is deformed simply to the 

nearest point of the polygon data, the area of the 

feature may be out of alignment, creating distortion 

on the surface after deformation and the fillet's 

boundary line may be disrupted, affecting the 

adjacent planar surface. The Fit feature in CADdoctor 

determines the transformation orientation with 

respect to curvature change of both the CAD and 

polygon data and maintains the correct position of 

the boundary line between the fillet and adjacent 

surface, thus ensuring continuity between faces that 

are maintained when deforming. 

For the specific area where the fitting is very 

difficult due to the large difference, the Reverse Engineering 

capability can only be used to create CAD data from polygon 

data. In summary, the fitting is completed for the polygon data 

and CAD data including the large difference area, by combining 

these methods. 

For more information, please visit the ELYSIUM website: 

http://www.elysiuminc.com 

For further information on CADdoctor in Italy, please contact: 

info@enginsoft.it 

This article was written in collaboration with ELYSIUM Co,Ltd. 

Akiko Kondoh 

Consultant for EnginSoft in Japan 

EnginSoft partners with ELYSIUM Co.Ltd. Japan 

to promote CADdoctor in Italy 

Figure 4: Distance between the original CAD data and the measured polygon data (left); 

distance after fitting (right)

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

�� 

��


Parametric FEM model optimization for 

a pyrolitic Indesit oven 

2009 Ecohitech Award: 

Recently, Indesit Company 

has won the prestigious 

Ecohitech Award and thus 

earned itself an “eco-virtuous 

enterprise" status. 

By examining only the internal 

glass of the pyrolitic oven 

which consists of visco-elastic 

material, the optimization 

process obtained the minimum 

stress distribution and stress 

gradient. 

To successfully finalize the work 

and to deliver the best possible 

technical results insuring 

highest quality standards are 

met, the following analyses 

have been performed by Indesit 

and EnginSoft: 

1) Parametric FEM model creation with ANSYS 

2) Creation of workflow in modeFRONTIER and ANSYS 

integration into Frontier’s loop 

3) Optimization of the clamping system by 

an automated routine defined within 

modeFRONTIER 

4) Results analysis and optimum design 

extraction according to the given 

objectives 

The present device belongs to a new type 

of the Indesit domestic oven range, called 

Pyrolitics. 

Indesit’s new technology allows a fast 

cleaning of oven cavity, by means of a 

pyrolysis process that burns encrustation 

Picture 2.2.1 – Temperature measuring 

point on internal glass 

caused by cooking. The Pyrolysis process starts at 

temperatures close to 500°C which are extremely high for a 

traditional device considering an external temperature of 

20°C. This environment produces an high thermal gradient 

which considerably deforms the glass. 

The door structure of the oven is made of a triple-glass 

system, whereas each is separated by an air wall to guarantee 

rapid heat dissipation and to respect the safety regulations 

which limit the allowed external glass temperature to 60°C. 

Glass stresses are derived from the thermal gradient, 

established between its surfaces, and produce a consequent 

deformation; an inappropriate glass clamping system would 

probably increase internal stresses and cause rupture. 

From experimental tests, we have learned that the internal 

glass is exposed to the highest stresses; in fact, this is the 

component with higher thermal gradients between its faces. 

The aim of this work was to develop a methodology that allows 

to simulate the real working conditions of the glass and to 

find an optimal glass clamping solution that minimizes the 

stresses. 

2 Structure of the model 

2.1 Solid model 

The model provided by Indesit has been made of a 3D door 

model of the oven with the actual glass clamping system. The 

door is composed of a 3 glass system, mounted on a specific 

structure that keeps them parallel and separated in order to 

allow the passage of the air cooling flow. This model has been 

simplified in order to obtain a complete glass clamp system to 

reproduce the real door-clamping solution. 

The provided material included some 

elements, such as, chamfer and a nonfunctional 

fillet that have been deleted in 

order to create a simplified model far 

easier to analyze. Constraints 

characteristics and glass geometry have 

been maintained in order to produce a 

suitable approximated model. 

2.2 Experimental measures 

After some experimental measures, a series 

of grid-organized values of temperatures 

on the internal glass of the oven, was provided by the user. 

These glass temperatures were obtained by some 

thermocouple probes on the point highlighted in picture 

2.2.1. 

Many repeated tests were performed in order to minimize the 

error of measure, and an average value of each measuring 

point was taken into account. 

In this verification, we have considered the maximum 

measured values to reproduce the worst working condition. 

3 Glass modeling 

3.1 Thermal modeling of the glass 

In order to perform a FEM analysis, it was necessary to assign 

to each node its temperature, but we had only eight measured 

points, that is why, the available value was modeled by using 

a RSM application. In fact, we used the eight measuring points 

to build an opportune RSM that reproduces the glasstemperature 

distribution with a good approximation.


Picture 3.1.1 – Approximating function and error graph (relative and absolute errors respectively) 

A Response Surface, also called meta-model, is a postprocessing 

tool of modeFRONTIER; in this application an 

approximate RSM was chosen, because all measuring points 

may be affected by a measuring error, due to uncontrollable 

thermal effects (e.g.: radiation and convection). 

In picture 3.1.1, an approximate function and relative 

approximation error graph are shown. 

Apart from obtaining a continuous tool able to estimate 

temperatures of all values including a variable space 

definition, using modeFRONTIER allows to obtain an analytical 

form of this surface.This expression will be used in the FEM 

modeler (ANSYS) to assign temp value on each node. 

Picture 3.1.2 – Constraint system of the glass 

The next step is the application of the analytical expression to 

the FEM model. In picture 3.1.2 we observe the glass with the 

applied temperature. 

3.2 FEM Model 

During the FEM modeling process, free glass deformation was 

evaluated firstly, or the maximum deformation reached 

without any constraint. 

During the next step, a series of constraints was applied on 

the glass, in order to compare the real glass deformation with 

the simulation and to estimate the model reliability. 

3.2.1 Free glass deformation 

By using ANSYS Multiphysics as finite element solver, only a 

corner was bonded and thermal field was applied in order to 

allow any deformation due to the thermal gradient. 

The thermal gradient originates from a difference in 

temperatures between contiguous areas; to perform the 

analysis we should know the values on 

both glass sides. 

The door of the oven is composed of 

three glass sheets spaced by few 

millimeters to allow an air cooling 

passage, this eliminates the installation 

of probes on the internal sides of the 

glass. 

To obtain all necessary temperature 

values and to perform our analysis, we 

had to model the whole multiple glass 

system, considering convecting effects; 

the known temperatures were from the 

measured set on the first internal glass 

face and a reference temperature of 60°C 

was established. 

Once the estimated necessary temperature values were 

defined, we have modeled a single bond on an edge of the 

glass. We knew that this was an unfeasible solution but it was 

necessary to understand the entity of the maximum glass 

deformation with this temperature field. 

By applying the calculated temperature function on the first 

glass, simulating heat transfer from the oven cavity to the 

room and calculating the thermal gradient on the component, 

we were able to obtain the maximum deformation of the glass 

in free conditions. 

The results show that the maximum deformation is 

concentrated in the center of the glass, as expected. The value 

of this deformation is aligned to the experimental results. 

3.2.2 Constrained glass deformation 

The initial complete model has been simplified in order to 

speed up the simulation, as detailed in par. 2.1. 

The constraints applied to the internal glass for the simulation 

of the real condition are: 

Picture 3.1.2 – Glass surface with the applied node-temperature 

Upper support 

Side support 

Back support 

Lower support 

Picture 3.1.2 illustrates the constraint system with and 

without glass. The upper support block YZ glass displacement, 

the side support block XZ displacement and the lower support

Pict. 4.1.1 – modeFRONTIER’s workflow 

Picture 4.2.1 – History chart SX 

block XY displacement. The constraint conditions have to be 

understood with a little tolerance in displacement. In fact, 

every constraint allows a clearance to avoid stress 

concentration due to an over- constrained condition. 

Applying the temperature field to the modeled system as 

described before, we continue with the structural simulation 

to calculate the stress on and the deformation of the examined 

component. 

In order to avoid value distortion, due to mesh problems, 

instead of considering maximum and minimum values, we 

have taken into account a mean value of this quantity close 

to the glass constraints. 

4 Optimization of the glass support 

The initial model described previously has been parametrized 

to allow the management by modeFRONTIER; the described 

parameters refer to the dimensions of the upper and lower 

glass constraints. While we focused on these constraints, the 

distances from the left and right glass edges and their width 

were parametrized. 

The aim of this step was to define an optimum set-up of the 

constraint system that minimizes the glass deformations in 

pyrolysis conditions. 


4.1 Project set-up in modeFRONTIER 

Variables used in this first optimization sub-step are therefore 

four and each couple refers to the dimension of a constraint. 

The constraints on the glass are four, symmetrical, and hence 

it is sufficient to modify the dimensions of only one to modify 

the couple: these will be the variables of the optimization. 

Lower and upper bounds of all the variables were set according 

to the customer’s requirements. 

By using modeFRONTIER, we want to manage the entire FEM 

(ANSYS) process automatically, to obtain the desired results. 

To interface the FEM model with the optimizer, some macros 

were built, or rather a series of pre- and post-processing 

instructions to modify the geometry of the model during each 

simulation. 

During the set-up of the optimization, some factors, such as 

time for each calculation or maximum available time have to 

be taken into account in order to define the best strategy. 

In this project, the time for each calculation was about 75 

minutes, not negligible; this made us choose a genetic 

algorithm that has a good robustness to find the optimum. 

The objectives were: 

Minimization SXZ shear stress; 

Minimization SX normal stress; 

Minimization SZ normal. 

The chosen algorithm was the MOGA (Multi Objective Genetic 

Algorithm), starting from an initial random population (DOE) 

of the input variables domain. 

Simulation parameters: 

MOGA iterations: 10 

DOE dimensions: 12 - variables number multiplied for 

objectives 

With these settings we have to do 120 runs for a total run 

time of 150 hours 

4.2 Optimization results 

After the optimization process, a good convergence of results 

was achieved: values of shear and stresses decreased up to 

40% with respect to the original configuration. 

Picture 4.2.1 shows an example of the history charts of 

stresses SX. 

As this is a multi-objective optimization, optimum results are 

more than one: in fact, we could have some designs which 

achieve the first objective, but are very far from the other 

objectives. Hence we are looking for the best tradeoff! 

In this job, all three objective are very correlated, so the 

convergence is parallel, which allows us to choose two 

optimal designs. 

From the obtained results we can extract some important 

information about the component behavior in real working 

conditions, especially with regard to the glass constraints 

dimension and their dispersion across the oven door: 

Distance of the lower constraint from the edge of the 

glass seems to have no influence on stresses; 

Width of lower constraint should be bigger than original; 

Distance of the upper constraint from the edge of the 

glass seems to have no influence on stresses; 

Width of upper constraint should be smaller than original;


In summary, for an optimal solution, the constraints layout 

should encompass the upper constraint going more close 

with the opposite behavior for the lower constraints. In the 

following picture the optimal solution is graphically 

represented. 

For the stresses, without having sufficient information about 

the glass characteristics, it is more opportune to present the 

deformation chart of the glass, during the pyrolysis phase. 

5 Conclusions 

The provided model is composed of an assembled system of 

three glasses, mounted on a chassis that keeps them 

separated to allow an air passage between them, in 

accordance with the regulations for this appliance. 

Experimental tests performed by Indesit are focused on 

temperature measurement of pre-determined points located 

on the internal side of the first glass, in pyrolysis conditions, 

when the internal temperature of the oven rises to 500°C. 

Punctual values of temperature, were computed with 

response surface modeling in modeFRONTIER in order to 

obtain a function that describes the temperature distribution 

on the entire glass, and assigns a relative value on each FEM 

model node. 

The built map is related to the hot side of the considered 

glass. To calculate temperature distribution on the cold side, 

Picture 4.2.2 – Displacement sum 

Picture 4.2.3 – Elongation due to the shear SXZ 

the entire glass system was modeled by thermal analysis. 

Knowing the internal cavity temperature distribution, the 

safety temperature on the external side of the outdoor glass 

and convex thermal coefficients, we were able to obtain the 

temperature distribution on the coldest side of the most 

stressed glass and hence also the thermal gradient applied to 

this component. 

The focus of the first simulation was on examining the free 

constraint condition of the glass, or to verify the maximum 

deformation of the glass, without constraint. 

In the subsequent simulations, the initial configuration, as 

described in the initial 3D model, was modeled with the dual 

purpose to validate the FEM model with experimental results, 

and to determine stress and deformation values of the initial 

configuration. 

The aim of the optimization process was to find an optimal 

layout of the constraint system that minimizes stresses on 

the internal glass. To achieve this result, the FEM model was 

parametrized by means of a series of instructions named 

“macros”, to allow modeFRONTIER to manage the geometry of 

the model. 

The task of modeFRONTIER is to modify the model geometry 

on each run and to drive the input variables to the best set. 

The modified parameters are referred to as the upper and 

lower glass constraints dimension, and in particular, the 

reciprocal distance and the width of each constraints are 

verified. 

The results were the values of stress and deformation on the 

model, due to the thermal gradient applied. Due to 

imperfections in the mesh, the mean value of stresses close 

to constraints, was taken into account. 

Obviously, the selected area for the calculation of this mean, 

was related to the area affected by higher stress values, to 

be precautionary. 

The obtained results meet our expectations: a sensible 

decrease of stresses was registered nearby 30-40% with 

respect to the customer configuration, and a good 

conversion of results was achieved, highlighting the good 

quality of the work performed by modeFRONTIER. 

The deformations of the optimized configuration are bigger 

than the original ones, which is an indication that the 

obtained design provides room for a better movement for the 

glass. 

Finally, we are certain that the obtained results are sufficient 

and correct, and that this work has delivered further 

information and details about the system behavior to the 

modeFRONTIER users at Indesit Company. 

For more information: 

Ing. Nicola Baldecchi 

info@enginsoft.it


Robust Design Optimization of a 

Bumper System at Volvo Cars using 

modeFRONTIER 

70% are low speed crashes 

According to a recent survey by Volvo 

Cars Brand Experience Centre, low 

speed crashes represent over 70% of 

the crashes today. Typically crashes 

up to approximately 15 km/h are 

categorized as low speed crashes and 

are often caused by accidents during 

parking, queuing and braking 

situations. 

The components of the rear part of 

the car are highly integrated, making 

repairs very expensive. Therefore, 

both customers and insurance 

companies require that the damage of 

a low speed crash should be limited 

to a few components which are easy 

to replace. In order to minimize the 

damage to the car body, the rear bumper beam must be 

designed to absorb all the energy from a crash. Due to the 

complexity and cost of repairs, the optimization of the 

bumper system becomes a very important and challenging 

topic. 

Ever since its establishment, Volvo Car Corporation has put 

safety among its top priorities and recently a thesis work [1] 

on best practices for robust design optimization of a rear 

bumper beam was carried out. 

Figure 3: modeFRONTIER was used to automate the robustness study using LS-DYNA and 

METApost. In order to save computational cost, a submodel instead of a full vehiclemodel 

was used for the robustness and metamodel evaluations. 

Figure 1: Low speed crashes represent more than 70% of the crashes and combined with very high costs for 

repairs make robust design optimization extremely important. The study focuses on the bumper beam shown 

to the right. 

Figure 2: Driving backwards into a fixed barrier at 15 km/h, i.e. the Allianz test, without damaging the car 

body is one of the toughest requirements. The figure shows the CAE model built in ANSA. This model of a 

full vehicle was used for verification. 

Performance varies due to tolerances in production 

Using modern crash simulation software such as LS-DYNA, it 

is now possible to predict the behavior in a crash with good 

accuracy. However, everything that is manufactured has its 

tolerances on geometry, material properties etc which means 

that in practice a certain range of variation on the 

performance parameters always exists. Any small deviation, 

even a random noise, could influence the real crash, but may 

not be visible in the CAE analysis when nominal values are 

used for simulation. 

A robustness study looks into groups of 

simulations with different combinations of input 

parameters, to see if they give similar responses or 

not. Just as with the input parameters, it is 

important to identify the relevant and interesting 

output parameters which are then traced in the 

robustness study. The analysis will show how the 

performance varies due to scatter in the input 

parameters. 

Evaluation of robustness 

Performing a robustness study is both complex and 

expensive. Complex, since the crashworthiness is 

determined by variations in a large number of 

parameters, such as material properties of different 

parts, friction, impact angle and speed. Complexity 

includes both choosing the most influential 

parameters and implementing them for automatic 

evaluation. Expensive, since a single simulation


Figure 4: Linear correlations between the 10 input variables for the Latin Hypercube 

sampling. 

Figure 5: Correlation between input variables approach the ideal value of zero as the 

number of designs grows. A maximum correlation of 0.1 between two inputs is regarded 

as acceptable which corresponds to a requirement of approximately 75 to 100 samples. 

takes about 2 hours using parallel execution on 24 CPUs and 

a robustness study may need more than 100 evaluations. 

The selected input parameters in this study are: 

Material properties of the bumper beam 

Thickness of the bumper beam 

Material properties of the parts behind the bumper beam 

Barrier impact and tilt angle 

Friction 

The selected output parameters are: 

Maximum plastic strain in all parts except bumper and 

barrier 

Mean plastic strain in all parts except bumper and barrier 

Number of high plastic strain nodes in all parts except 

bumper and barrier 

Maximum deformation of the bumper beam 

Kinetic and internal energy of the model 

Maximum bumper beam internal energy 

Section forces of the side member 

Latch displacements 

The preferred sampling method for this type of robustness 

study is Latin Hypercube. A central question is how many 

samples are needed for the chosen 10 variables in the study. 

A possible answer is to study the correlations between the 

input variables as shown in figure 4. Figure 5 shows the 

absolute max and arithmetic mean of the correlation versus 

the number of designs. It can be seen that both values 

approach the ideal correlation of 0 as the number of designs 

grow. A correlation of 0.1 is regarded as acceptable 

which corresponds to about 75 to 100 samples. In 

the crashworthiness study, the complexity of the 

evaluated results as well as the number and 

complexity of significant interactions among the 

input variables may require even more samples to be 

evaluated in order to reach converged stochastic 

results. 

In this study, convergence of the stochastic results of 

the initial sampling of 200 design points is verified 

by an additional 100 design points. The additional 

100 designs are also generated from Latin Hypercube, 

but from a different random seed. This means that 

the additional 100 designs differ from the original 

200 designs and the 300 designs as a whole still 

follow the Latin Hypercube space filler distribution. 

It is observed that there was not a big difference 

between the output correlations or the output 

distributions gained from the 200 and 300 design 

sets. 

Results of the robustness study 

One result of the robustness study is a list of the 

main effects for each results quantity. Figure 6 shows 

the effect of input parameters on the maximum 

internal energy of the bumper beam, ranked from 

most to least influential. It can be seen that the 

maximum internal energy of the bumper beam is critically 

influenced by changes to the tilt and impact angle of the 

barrier. In addition, an increase in the friction and a decrease 

in the bumper beam material strength could give higher 

energy absorption. 

Besides, the effect of each individual input parameter 

interactions of several inputs can be significant. As it can be 

seen in table 1, the combination of material properties of the 

rear side members and the impact angle have more effect on 

the results than the single factors friction or material 

properties of the bumper beam. 

Table 1: Comparison of main and interaction effects of the inputs on 

maximum level of the bumper beam internal energy. 

The robustness study also uncovered a set of designs giving 

extreme results. A separate study on these outliers revealed 

that they all had low values of friction. The root cause of the 

outliers is related to the way LS-DYNA deals with friction. As 

a result, 200 new FE simulations were performed with the 

friction fixed at the nominal value. The ranking of main and 

interaction effects was not affected while the output values 

and their distributions had to be updated. Table 2 shows how

the most important stochastic data changes when 

friction is removed as a stochastic input variable. 

The table also shows that the standard deviation 

of the internal energy is in the order of 5-10% of 

the nominal value. By comparison, the number of 

deformed elements, i.e. elements exceeding a 

specified plastic strain, has a standard deviation 

exceeding 50% of the nominal value. 

The correlation chart is a versatile tool and figure 

7 shows the original 10 input variables versus 4 

outputs. Marked boxes are regarded to have high 

values of correlation. Since the variables Tilt, 

Thickness, Impact Angle and Friction have many 

marked boxes but only one box is marked for the 

material properties, it is concluded that variations 

in material properties are of less importance than 

variations in the loading case. 

Another important result is the correlation 

between the outputs. Figure 8 shows that an increase in the 

maximum internal energy of the bumper beam leads to a 

decrease in the number of deformed elements on the ring 

frame. 

Table 2: Variation of friction has a significant effect on some of the 

stochastic results. It is also clear that the robustness properties can hardly 

be ignored when the maximum value in the study exceed the nominal value 

by more than 5 times. 

The necessity of metamodels 

As seen in the robustness study, the scatter of 

the results cannot be neglected in an 

optimization. Furthermore, the computational 

expense makes it most desirable to find a fast 

replacement for the FE simulation during the 

optimization. In modeFRONTIER there are 7 

types of metamodels which aim to replace the 

underlying simulation model with a very fast 

but approximate function. The evaluation time 

is in the order of 0.05 seconds, making it 

possible to evaluate thousands of design 

candidates in order to solve the robust design 

optimization task. 

The process of using metamodels is divided into 

3 steps: 

Training the metamodel 

Evaluating the quality of the fit 

Using of the metamodel 

It was not obvious which metamodel would 

deliver the best fit so Kriging, Radial Basis 


Figure 6: The main effects plot shows that the most influential parameter on the internal 

energy of the bumper beam is the tilt of the barrier followed by the impact angle and friction. 

Function and Neural Networks were included and evaluated. 

Besides the previously mentioned robustness parameters, 3 

new geometry parameters, implemented through mesh 

morphing in ANSA, were introduced. 

The training set consisted of 1000 FE simulations and 

another 170 FE simulations were used to check the quality of 

the metamodels. Figure 9 shows the difference between the 

Radial Basis Function and the evaluation set for one of the 

results. The mean residual values between the three methods 

were close and the response looked similar to the same 

design IDs. As such, all three methods in this study are 

considered to give equally good results. In the end, the 

parameters given by the Neural Networks were chosen for 

final verification. 

Figure 7: Correlation between input and output variables. The variation in crashworthiness due to 

scatter in material properties is small if compared to the scatter in the load case variables.


Figure 8: Correlation between output and output variables. An increase in the internal energy 

is strongly correlated to fewer nodes with high strain in the ring frame. 

Figure 9: The residual chart shows the difference between the forecasted value by the Radial 

Basis Function and the FE simulations for the evaluation set. 

Table 3: The optimized bumper has been improved in all studied outputs. 

Robust Design Optimization 

The metamodels were used to 

run a multi-objective robust 

design optimization. A design 

found through optimization on 

the metamodels was then 

selected and verified using real 

FE simulations. Table 3 shows 

results for highly strained 

elements and it is clear that the 

optimized bumper beam is a big 

improvement over the original. 

Both the mean value and 

standard deviation have 

decreased. The comparison is 

also done for the full car model, 

to confirm that results 

calculated from the submodel can be applied to 

the full car, cf. figures 10 and 11. 

The bumper which was optimized according to 

the Allianz load case was also tested in other low 

and high speed crashes. The results highlighted 

the necessity to consider multiple load cases at 

the same time during the optimization. 

Summary 

Overall the results were very promising, proving 

the potential of running robust design 

optimization on metamodels for crash 

simulations. The initial robustness study also 

provided great value and insight into the 

dominant parameters and considerations 

regarding the FE simulations. The arithmetic 

mean and standard deviation for the stochastics 

simulations were improved for all studied 

outputs, e.g. for the ringframe the results were 

improved by about 50% and 20% respectively. 

Reference 

[1] Xin Li and Tolga Olpak, "Robustness and 

Optimization Study of a Rear Bumper Beam 

During a Low Speed Impact", M.Sc. Thesis at 

Volvo Car Corporation, Göteborg, Sweden, 

Department of Solid Mechanics at the Royal 

Institute of Technology (KTH), Stockholm, 2009 

Authors 

Dr. Anneli Högberg, CAE Crash Engineer, Volvo Car Corporation, 

ahogberg@volvocars.com 

Ass. Professor Martin Kroon, Department of Solid Mechanics, 

Royal Institute of Technology (KTH), martin@hallf.kth.se 

Xin Li, CAE Engineer, FS Dynamics AB, xin.li@fsdynamics.se 

Tolga Olpak, CAE Engineer, Xdin Systems AB, 

tolga.olpak@xdin.com 

Håkan Strandberg, Sales Manager, EnginSoft Nordic AB, 

info@enginsoft.se 

Figure 10: a) shows the plastic strain on the ring frame (i.e. a rear part of car body) in the submodel with original 

bumper beam. b) shows the plastic strain on the ring frame in the submodel with optimized bumper beam. 

Figure 11: a) shows the plastic strain on the ring frame in the full car model with original bumper beam. b) shows 

the plastic strain on the ring frame in the full car model with optimized bumper beam.


Optimization in product development - 

An efficient approach to integrate single 

CAE Technologies up to the entire 

design chain 

Overview 

In today’s industrial production plants, state-of-the-art 

software systems are used to analyze different loading 

conditions in order to determine the performance and 

durability of a product. Similarly, production companies 

use simulation for manufacturing processes, such as 

casting and welding. Optimization techniques are widely 

regarded and applied as the next logical step to perfect 

competencies in simulation for modern product 

development. Possible applications of optimization 

techniques range from local problems with single 

applications up to the mapping and optimization of a 

large range of parameters of an entire product 

development process. Hence optimization can provide 

significant time and resources savings, opportunities that 

are illustrated in this article. 

Introduction 

Since the introduction of the computer, nearly all areas of 

life have changed rapidly. This applies also, and in 

particular, to the working environment and all professional 

activities of engineers. 

For example, engineering 

drawings are no longer 

made on a drawing board 

using 2D techniques; 3D 

models are created 

instead on the screen. 

Thus necessary 

adjustments to the 

product are realized 

quickly, for example the 

weight or the moment of 

inertia of complex 

Figure 1: Stress analysis of a crank shaft 

Figure 3 (a) Solidification stage of a casting simulation and (b) forging simulation of a crank shaft 

Picture 2: CFD simulations for a turbine blade 

geometries can be determined – all in an automated way. 

Advances in computational mechanics, such as the FEA 

Finite-Element Method, have also made their way into 

modern production facilities a long time ago. Again, clear 

advantages of simulation are shortened product 

development cycles, improved assessments of product 

quality and, importantly, savings in experimental time and 

equipment. 

Today’s status of simulation in product development 

covers a number of standard analyses, including: 

Strength and durability/fatigue analyses of mechanical 

and/or thermally stressed devices in most diverse 

loading conditions (Figure 1), 

Computation of characteristic measures in CFD 

problems as shown in Figure 2, 

Crash Simulations in the area of Safety Engineering and


Vibration and dynamic analyses of 

complex multi-body models. 

Considering its industrial infrastructure, the 

area of manufacturing process simulation 

could be regarded as a separate domain of 

computation. The attention here is not 

purely focused on the product, as also the 

required tools for the processes have to be 

taken into account. Those simulation 

methods comprise among others: 

Simulation of casting processes 

including filling and solidification 

processes, the resulting impacts on the 

material microstructure and the 

corresponding local mechanical 

properties as well as the residual 

stresses (Figure 3a), 

Simulation of forging processes with 

forming simulations performed continuously or in 

several steps, including material and stress-strain 

analyses of the device and the forging dies (Figure 3b), 

Injection-Molding Simulation of plastic-based devices 

including filling and solidification processes as well as 

joint formation, 

Simulation of machining processes including chipforming 

analysis, thermo-mechanical analysis of the 

material removal rate of the workpiece and the tools as 

well as of surface properties. 

If we consider the structural trends in manufacturing and 

R&D industries as an example - the ever-growing global 

competition, shorter development cycles and increasing 

demands on product quality to name a few - it is evident 

that further efforts are necessary to reduce costs and 

improve product quality. This is particularly important for 

companies whose operations are based in technologically 

Figure 4: Parameter Optimization of a bicycle frame 

advanced countries, such as Germany. Here, the CAE 

application “Optimization” is a well-known common 

practice and among the primary goals of technical 

developments. 

Optimization 

Optimization is defined as the mathematical process for 

finding optimal parameters of mostly complex systems 

with regard to a single or multi-objective functions. It is 

important to understand the advantages of optimization 

which are explained hereafter with the help of some 

examples: 

Target functions depend on individual problems and, in 

reality, often conflict with each other. Therefore, the 

ultimate objective of optimization is to find a solution 

which represents the best compromise among the 

different objective functions. 

Due to its mathematical background and its 

Picture 5: (a) The modeFRONTIER Workflow which integrates a FEA application for a strength calculation of a bicycle frame. (b) The results of the optimization 

run presented in a Bubble Chart with the highlighted Pareto Frontier.

independency from respective applications, 

optimization is often regarded as a complex and 

independent field of action. Thereby, commercial tools, 

such as modeFRONTIER, are readily available for use 

since a long time. Such tools allow to setup, perform 

and automate optimization analyses in an easy way. 

The optimization level (and, hence, potential savings) 

depends to some degree on the development status of 

a company. On the one hand, it is possible to perform 

optimization on a relatively low level for the 

Figure 6: Optimization of a support roller of a paper machine 

parameters of a single product. On the other hand, 

optimization can be considered as a tool of process 

integration and automation, hence, to enable the 

mapping and simulation of the complete process and 

design chain. 

Optimization of a bicycle frame 

Figure 4 illustrates an optimization of a bicycle frame with 

relatively traditional optimization objectives in structural 

mechanics: The goal here is to minimize the stresses 

caused by different loading conditions; at the same time, 

the weight of the frame should be minimized. Moreover, 

requirements regarding limits for maximum stresses 

(tensile strength and fatigue resistance) have to be 

observed. 

In this example, the available geometric optimization 

variables are some lengths, the thicknesses of the tubes 

and their radial dimensions. In fact, with modeFRONTIER 

the present problem can be described in a single run and 

by integrating a single FEA application, as shown in Figure 

5. Here, after an automatic analysis of the problem 

structure, modeFRONTIER recommends to run the 

optimization with a certain algorithm - in the present 

case a Multi-Objective Genetic Algorithm MOGA-II, with 

an appropriately generated DOE. 

The optimization run takes place automatically and allows 

a systematic Illustration of the results as, for example, by 

using a Bubble Chart as shown in Figure 5 (b). Here, the 

optimal solutions on the Pareto Frontier are clearly visible. 

In this example, the automation enabled the engineer to 


compute 300 designs within a few minutes time. Hence, 

the design time was shortened, instead of wasting time 

for multiple manual variations. Additionally, the 

performance of the bicycle frame with respect to stresses 

could be improved, while achieving significantly lower 

weight conditions, which also led to lower material costs. 

Design Chain Optimization 

The relatively simple optimization approach applied to the 

design of the bicycle frame already delivered significant 

savings. This approach however is based on the (mostly 

feasible) assumption that existing residual stresses, σ0, 

inside the device can be neglected. These stresses derive 

from upstream manufacturing processes. With regard to 

the bicycle frame, we could consider such stresses being 

related to welding, heat treatment, and quasi-static 

bending (straightening) processes of the frame. If 

available, this data could be used in a subsequent stress 

analysis to take into account real initial stress conditions 

and thus provide a far more accurate optimization. This 

way, we would obtain a process chain with four different 

applications which also can be mapped and optimized in 

modeFRONTIER. 

As another similar example, we can take a closer look at a 

roller support of a paper machine, as illustrated in Figure 

6. The roller support is manufactured by a casting process, 

the weight of the first design was 476 kg. The 

optimization goal here was to minimize the weight and 

deformation at the same time. In addition, the castability 

of the final form had to be guaranteed. 

In this example, the sole and initially performed 

optimization of the geometry (variation of 13 parameters) 

with respect to the most extreme load-case delivered a 

weight reduction from 476 kg to 360 kg, while the 

deformation was reduced slightly. The verification of the 

castability was performed using the software tool 

MAGMASOFT (sand casting) in a second step after 

optimization. 

Analyzing the casting simulation, the results additionally 

revealed zones with non-homogeneous microstructure and


Figure 7: Standard Optimization (left) in comparison with an Optimization which encompasses the entire process chain: at the critical points, the analysis 

that considers casting simulation shows increased van Mises stress values. 

hardness due to different thicknesses and local cooling 

rates. Also, local zones with high residual peak stresses 

were found, which have a decreasing effect on the fatigue 

life of the roller support. 

These results gave reason to consider performing a largely 

extended optimization analysis that includes both the 

casting simulation and load-case analyses. In a tool such 

as modeFRONTIER, the complete process chain could be 

setup, in which results of the casting simulation are 

transferred as initial conditions to the subsequent FEMbased 

load-case simulation. Hence, all following steps are 

included in this kind of optimization problem: 

Casting simulation with MAGMASOFT to ensure the 

quality of the materials, to avoid casting defects, 

determination of local material properties (for example 

Young’s module, fatigue and yield stress limits), as well 

as residual stresses on the different roller support 

zones. 

Transfer of the results via MAGMAlink (residual stresses 

and material properties) as initial conditions to be 

used in ANSYS. 

Load case (stress-) analysis with ANSYS. 

This procedure enables also the systematic optimization of 

the support roller geometry with respect to the load in 

operating conditions, but including the consideration of 

residual stresses and the locally changed material 

properties from the casting manufacturing process. The 

castability could, therefore, be guaranteed reliably. 

Additionally, statements with respect to the fatigue life of 

the product could be obtained and coupled to the 

optimization procedure as constraints. 

Figure 7 shows the original (traditional) load case analysis 

(left) and an excerpt of such a novel design chain 

approach that considers the results from the casting 

simulation (right). It is clearly seen that the stresses in 

the roller support are in no way homogeneously 

distributed due to different pre-stress conditions and non- 

homogeneous mechanical material properties. Similarly, 

peak stresses (van Mises) can be seen to be increased in 

some areas from approximately 30MPa to 50MPa (166%). 

Maximum principle stresses (not shown) even highlight 

increased values from 60MPa to 228MPa (380%). Although 

these values are yet far away from the materials tensile 

and fatigue stresses, they lead to significant reductions in 

the fatigue life of the product. 

Conclusions 

The ever growing competitive global market place will call 

for more and more applications of optimization techniques 

in various industrial sectors. In this article, we have 

outlined the following key points: 

The optimization of real problems most often defines 

solutions which are in conflict with each other. Such 

Multi-Objective Optimization tasks can already be 

solved today with easy-to-use software, such as 

modeFRONTIER. 

It is possible to perform automatic optimization 

already for simple development cases by linking 

standard tools from arbitrary areas (e.g. CAE tools). 

Optimization can be extended infinitely and, hence, be 

regarded as a tool for process integration and 

automation. In this way, it is possible to setup 

simulations of an entire process chain and, therefore, 

to systematically extend the optimization capabilities 

from single device parameters to the parameters of the 

entire design chain. 

There is potential for large savings. They may comprise 

in experimental costs and reduction of development 

times due to the automation of computations. Thereby, 

savings even go hand-in-hand with ensuring product 

quality. 

Hans-Uwe Berger, EnginSoft GmbH, Frankfurt am Main 

25. Schmalkaldener Fachtagung/Conference: 

Die Digitale Fabrik–Module und Referenzlösungen/Digital 

Plant – Modules and Solutions


ANSYS simulation of carbon fiber and 

anisotropic materials 

Introduction 

The scope of this R&D is to develop a new 

support, with an integrated cooling system, for 

the replacement of the inner layer of the 

Silicon Pixel Detector installed into the ATLAS 

Experiment, working on the Large Hadron 

Collider at CERN; for details, we ask our readers 

to visit: www.atlas.ch/pixel-detector.html. This 

replacement will become necessary because of 

the radiation damage, with the detector being 

very close, about 50 mm, to the high-energy 

proton-proton interaction point. 

The task of the support system is to hold the 

detector modules in positions with high 

accuracy, minimizing the deformation induced 

by the cooling; this must be done with the 

lowest possible mass because there are tight 

requirements in terms of material budget. An 

evaporative boiling system to remove the 

power dissipated by the sensors is incorporated in the 

support: thermal contact is made through a very 

conductive light carbon foam to maintain the sensor 

temperature sufficiently low, to limit the leakage currents 

and hence the thermal run-away. The coolant should be a 

fluorocarbons blend or CO2. The worst case is imposing a 

cooling pipe design pressure of 10 MPa. The number of 

Prototype of a stave with 2 carbon fiber pipes integrated into the carbon 

foam and attached to the structural omega shaped laminate. 

The ATLAS Pixel Detector during construction. Here we can see one of the cylindrical shells of 

Pixel detectors formed by the longitudinal cooled supports called staves. 

pipes could be 1 or 2 and the pipe material should be 

carbon fiber or titanium. The structural strength of the 

800 mm long support stave is given from a carbon fiber 

“omega” shaped laminate. 

Summary of the work 

The design is based on thermal, mechanical and thermostructural 

analyses of assemblies made of carbon fiber 

composites. Calculation of the Tsai-Hill safety factors and 

transversal strains in the 

plies are required for 

tightness assessment of 

the pipe. Moreover, the 

pipe lay-up optimization 

against the internal 

pressure has been made 

together with estimations 

of the thermal expansion 

coefficient of the pipe 

and omega laminates. We 

used ANSYS and 

ESAComp; input figures 

Carbon fiber pipe production test using 

braiding technology, before 

impregnation with resin 

for the ply properties, starting from fiber and matrix 

values, are provided by a dedicated spreadsheet. To 

validate the FEM simulations both Composite Laminate 

Theory hand-made calculations on cross-check simple 

models and experimental tests are used. Work is still in 

progress to measure material characteristics and FEM 

results: pull test on pipes performed with “braided” 

technology, burst pipe pressure, thermal transmission


Cross section example of a finite element model. Note the mapped mesh for the laminate pipe and omega, whose one possible stacking sequence is showed. 

coefficient K of the carbon pipe, CTE and deformations 

induced by the cooling using a Coordinate Measuring 

Machine. 

The R&D key element is the production of the CF pipe and 

of the relative joints versus the external connecting 

piping, having suitable mechanical and tightness 

properties. 

FEM of composite materials 

Some assumptions are taken up in building the model and 

some parameters needed to run the software should be 

guessed as they are absent in literature (i.e. ply out-ofplane 

moduli and Poisson coefficient). A major problem 

found in building the models is the necessity to correctly 

orient the layered elements for the composites which 

turns out to be very time consuming. Moreover, in the 

multi-physics, during the switching from structural to 

thermal analysis automatically a different orientation of 

the thermal element coordinate systems is set; the use of 

dedicated APDL macro routines can be useful to optimize 

the FEM workflow. We used several meshing techniques: 

mapped mesh for composites materials and free mesh in 

Thermal solution example 

Internal pressure and longitudinal stress applied to the pipe 

2D or extruded mesh in 3D for the anisotropic materials. 

Geometry of the anisotropic carbon foam has been 

carefully conditioned in order to avoid degenerated shape 

elements. We have chosen to assemble models, avoiding 

the use of contact elements between the meshed parts in 

order to obtain a practicable linear solution method, 

merging the interfacing nodes and reducing both the 

number of elements and the run-time. Comparisons 

between different sized meshes, with aspect ratio 

ranging from 1 to 10, and between 2D cross section 

and 3D solutions have been judged for time 

optimization and control purposes. 

The use of brick elements for thin solids was driven by 

our specific multi-physics needs. 

Note that the composite pipe produced by the 

“braiding” technology can only in first approximation 

be simulated by the laminate multi-ply hypothesis, 

like those implemented in the layered elements 

available at present. This could be an interesting 

ANSYS product development. We are also in contact 

with the DIGIMAT micro-mechanics developers to 

study the problem.

Thermal performance 

The thermal performances of the different configurations 

proposed are studied with steady-state 2D simulations. Heat 

flux is applied while the BC is the temperature setting of the 

cooling pipes inner surface. We collected the resulting max 

ΔT across the staves in a table, using a performance 

parameter obtained by dividing ΔT by the thermal power flux 

imposed as load. 

Evaluation of the thermal expansion coefficients 

Longitudinal CTE is calculated for the possible 

configurations; the simulations are executed with the volume 

fiber percentage measured on the samples, ranging from 30% 

to 60%. The calculation procedure is to build a model and 

increase the nodal temperature in order to have a ΔT: the 

nodal displacement is evaluated and the relative CTE is then 

calculated. ESAComp has been used for cross check. 

Pressurized pipe lay-up optimization 

The design of the pipe laminate should satisfy these criteria: 

withstanding a pressure test of 15 MPa, having a safety 

factor of 4 on the design pressure against a Tsai-Hill failure 

criterion, matching the longitudinal CTE of the other 

materials, remaining tight under pressure with maximum 

transversal ply strain ≤ 0.1%. This is the parameter that 

controls the micro-cracks growth. Pipe is modelled using the 

element layered-type Solid186. Pressurized vessel conditions 

are simulated with axial force on the pipe extremities. 

Different pipe stacking sequences are considered for these 

structural simulations; for each ply longitudinal, transversal 

and shear stresses and strains are extracted for the result 

Thermo-mechanical simulation results for a given configuration. 

analysis, used directly or combined in the failure criteria. 

Comparison between the stress values or Tsai-Hill index 

resulting from the simulation and the corresponding rupture 

stress values of the ply is done. Lastly, the best lay-up, 

matching the requirements and including technological 

feasibility, is [45/-45]s. 

Deformations induced from gravity, cooling and pipe 

pressurization 

To understand the thermo-mechanical effects, we first 

performed 3D thermal simulations using 20 node Solid90 


elements, in order to determine the temperature field 

under defined heat flux. The resulting nodal temperatures 

have been imported, node to node, in the structural 

environment, using Solid186 elements to determine the 

deformations and stress of the stave components due to 

the thermal induced deformation, related to the different 

CTE values of the materials. Coefficients of thermal 

expansion of the ply are calculated by the Schapery 

formulas. In the following study the loads applied to 

analyse the behavior of the stave are: 1) cooling-down: ΔT 

= -60°C, that is the ΔT between the assembling 

temperature and the minimum evaporation temperature; 

2) static gravity to evaluate the maximum deformation 

due to the weight; 3) pressure 10 MPa inside the cooling 

pipe. 

Conclusions 

A number of considerations have been taken into account 

in the frame of this collaboration with regard to all ANSYS 

silmulation results and other parameters, such as the 

global radiation length, to optimize the assembly 

properties. The final choice to be made will also depend 

on the measurements in progress on the real prototypes. 

The ANSYS software can be used as a useful tool for the 

model analysis with composite and anisotropic materials. 

A lot of work has been devoted to understanding the 

method, and then to building the required models in a 

proper way, for achieving the various simulation goals. 

The real measurement performed on a pipe prototype, 

actually the CTE of a CF pipe, provides a first positive 

feedback from the R&D work which is still in progress. 

Acknowledgments 

Thanks to the colleagues of the INFN Milano Mechanical 

Design and Workshop Department, in particular Mauro Monti, 

the responsible for the simulations and to Danilo Giugni and 

the whole ATLAS Insertable B-Layer Collaboration. 

Ing. Simone Coelli 

Istituto Nazionale di Fisica Nucleare 

Sez. di Milano


Aeronautical engines: 

reduction of emissions and 

consumptions with a 

process simulation study 

The project “Marboré”, 

which is promoted by the 

Department of Mechanical 

Engineering of the 

University of Padua, aims 

at offering to aerospace 

students a trial aeronautical engine in order to carry out 

tests and researches. These studies are useful both to 

improve the performances of turbojets according to stricter 

laws for the reduction of CO2 and NOx emissions and to 

reduce fuel consumption. 

As the design of a new propeller involved many technical 

and economical difficulties, the University decided to use a 

turbojet which was already available on the market. 

At the end of 2006, a Marboré VI-C turbojet, dismantled 

from a target plane which had crashed, was collected and 

given to the University. The engine had been designed and 

produced by the French company Turbomeca in the 70’s. 

After the impact, the propeller was heavily damaged, in 

particular the front section, seat of the centrifugal 

compressor. The project included the entire modelling of the 

turbojet with the help of a CAD software and the 

reconstruction of the damaged parts. 

At the end of 2007, the rotor was completed, while the 

intake casing, originally created with magnesium alloy, was 

excluded from any analysis, as this study required particular 

knowledge about the casting process. For this reason, it was 

necessary to carry out a specific study for this part with the 

aim to find out all the technological details to plan and 

perform the casting process. 

The first step was a careful CAD modelling which was 

slightly modified from the original according to the 

different use and then the project focused on the design of 

the casting system. 

Motori Aeronautici: riduzione 

delle emissioni e dei consumi 

attraverso lo studio delle 

simulazioni di processo 

Il progetto Marborè, promosso dal Dipartimento di 

Ingegneria Meccanica dell’Università degli Studi di Padova, 

ha lo scopo di mettere a disposizione degli allievi aerospaziali 

un motore aereonautico funzionante a banco, su cui 

poter effettuare test e studi di ricerca volti a migliorare le 

prestazioni, come l’abbattimento delle emissioni inquinanti, 

quali NOx e COx, e la diminuzione dei consumi. 

Visto il notevole sforzo, economico e tecnologico, che sarebbe 

stato necessario per la realizzazione ex-novo di un 

propulsore su cui lavorare, si decise di utilizzare un turbojet 

già presente sul mercato. Verso la fine del 2006 viene recuperato 

e messo a disposizione dell’Università un turbogetto 

di tipo Marborè VI-C, realizzato negli anni ’70 dalla 

francese Turbomeca, smontato da un aereo bersaglio 

schiantatosi al suolo. A seguito dell’impatto, il propulsore è 

risultato essere fortemente danneggiato, soprattutto nel 

comparto anteriore, sede del compressore centrifugo. Il progetto 

prevede la completa modellazione del turbogetto, attraverso 

software CAD, e la ricostruzione degli organi danneggiati.

After a first sketch of 

the casting system 

using CAD, the 

software MAGMASOFT 

was used to verify and 

optimize the casting 

process. During this 

step an academic 

approach permitted to carry out a series of simulations 

which modified the model as to a careful analysis of the 

results. This enabled to obtain a single good quality 

prototype without limits on time and elaboration methods. 

First of all, the simulations of the initial versions, which 

were created in agreement with the partners involved in the 

project, were essential to choose among different possible 

configuration methods. These versions differed both in the 

cooling system and in the filters placement. 

The first version had a central cast iron chill and three 

exothermic feeders on top of the component. Solidification 

results immediately showed that this type of placement was 

perfect for the bearing support: as a matter of fact, a very 

quick cooling improved the mechanical characteristics in 

the most “significant” area of the component. At the same 

time, isolated liquid bubbles on the external surface during 

the cooling caused feeding problems. 


Alla fine del 2007 i componenti rotorici sono stati completati, 

mentre è rimasta esclusa da ogni tipo di analisi la bocca 

anteriore del motore, fusione monoblocco in lega di magnesio, 

studio che richiede particolari conoscenze del processo 

produttivo. 

Si è quindi reso necessario realizzare un lavoro specifico per 

questa parte, che approfondisse tutti i dettagli tecnologici 

per la progettazione e la realizzazione per processo fusorio 

del componente. 

Si è iniziata un’attenta modellazione CAD, con alcune lievi 

riprogettazioni dettate dalle diverse esigenze tra un componente 

progettato per il volo da uno statico da banco, soffermandosi 

poi sulla progettazione del sistema di colata. 

Dopo un primo abbozzo nell’ambiente CAD, si è passati a lavorare 

con il software MAGMASOFT, necessario per verificare 

e ottimizzare il processo di colata. 

Durante questa fase si è mantenuto un approccio di tipo accademico: 

si è realizzata una serie di simulazioni applicando 

di volta in volta alcune modifiche dettate dall’attenta 

analisi dei risultati ottenuti, ponendosi come unico obiettivo 

la realizzazione di un singolo prototipo di buona qualità, 

senza porsi limiti nei tempi e nei modi di elaborazione. 

Innanzitutto si sono implementate più versioni rappresentanti 

le varie configurazioni del sistema di colata inizialmente 

progettate in accordo con le parti partecipanti al 

progetto. Queste differivano nel sistema di raffreddamento 

e nella disposizione dei filtri di colata. 

Nella prima versione era previsto un raffreddatore centrale 

di ghisa e tre maniche esotermiche, in corrispondenza delle 

zone massicce. I risultati di solidificazione hanno evidenziato 

subito che tale disposizione era ottima per il supporto 

del cuscinetto, in quanto il raffreddamento repentino garantisce 

caratteristiche meccaniche migliori, mentre per 

quanto riguarda la corona esterna erano presenti notevoli 

zone di liquido isolate durante la solidificazione e quindi 

conseguenti problemi 

di porosità. 

La seconda versione 

prevedeva, invece, 

di utilizzare esclusivamente 

maniche 

esotermiche, disposte 

nella parte su-


In the second version 

there were only 

exothermic feeders on 

the top. Just like the 

previous version, this 

new one presented both 

pros and cons during the 

analysis of the 

solidification results. 

The fluid temperature 

was more homogeneous 

in the cooling but the cooling front on the bearing support 

moving upward and the longer time of solidification led to 

worse mechanical characteristics, especially on the central 

part. 

Both configurations, which were initially designed, enabled 

to obtain a “hybrid” system and simulations showed that 

this version was definitely better. It was constituted by a 

central cast iron chill and eight exo-feeders on the top of 

the component. In addition, the number of ingates 

increased up to six in order to improve the homogeneity of 

the input flow and to reduce the temperature gradients, 

which appeared at the end of the casting process in the 

previous versions with only four ingates. 

Filling and solidification results pointed out that feeding 

defects decreased in comparison to the previous versions, 

hence the quality target was reached. Defects could be 

considered irrelevant due to the precautionary software 

applied and the particular refining of the casting process. 

The sand mold was therefore realized using SLS (Selective 

Laser Sintering) rapid prototyping techniques. Afterwards, 

light alloy casting was carried out taking into particular 

consideration the preparation of the alloy. Finally, some Xray 

analyses were performed to verify the 

integrity of the component and to 

compare the simulation results with real 

data. This study enabled to analyse each 

detail accurately and to follow the 

transformation from a CAD drawing to a 

real component. In addition, it pointed 

out the potentialities of this process, 

which is suitable both to optimize all the 

steps using specific software and at the 

same time, to minimize errors. 

periore del getto. Anche in questo caso i risultati hanno 

messo in luce fattori positivi e negativi della configurazione. 

Si è ottenuta infatti una maggiore omogeneità del fluido 

in fase di solidificazione, peggiorando però le caratteristiche 

meccaniche, specialmente nel supporto centrale. 

Si è cercato quindi di unire le caratteristiche migliori delle 

due versioni, ottenendo un sistema ibrido che dalle simulazioni 

è risultato decisamente superiore rispetto alle precedenti 

disposizioni. 

Esso prevede l’utilizzo del raffreddatore in ghisa centrale e 

otto maniche esotermiche sulla corona esterna. 

Inoltre per garantire una maggiore omogeneità del flusso in 

fase di riempimento, si è scelto di aumentare a sei il numero 

di attacchi, in maniera tale da limitare i gradienti di temperatura 

presenti a fine colata nelle versioni con solo quattro 

ingressi. 

I risultati di riempimento e di solidificazione hanno sottolineato 

infatti che i difetti di microporosità sono diminuiti 

rispetto alle versioni precedenti, raggiungendo la soglia di 

qualità ricercata. I difetti ottenuti possono essere ritenuti 

trascurabili per la cautelatività del software e la particolare 

affinazione del processo produttivo. 

Si è quindi realizzata la forma attraverso tecniche di prototipazione 

rapida, utilizzando tecnologie SLS 

(Sinterizzazione Laser Selettiva). Compiuta la fusione in lega 

leggera, con una particolare attenzione alla fase di preparazione 

del metallo da colare, si sono fatte alcune analisi 

radiografiche per verificare l’integrità del componente e 

confrontare quindi i dati delle simulazioni effettuate con i 

dati reali. 

Diversamente da come avviene solitamente in contesto lavorativo, 

dove normalmente non si seguono 

tutte le fasi progettuali e realizzative, 

in questo studio è stato possibile 

analizzare accuratamente ogni dettaglio 

e vedere un disegno CAD trasformarsi 

in un componente reale. 

Inoltre questo percorso ha evidenziato 

le potenzialità di questo processo che 

permette di ottimizzare tutti i passaggi 

attraverso software specifici, riducendo 

al minimo i margini di errore.

Healing the swine flu with 

modeFRONTIER 

One of the hot topics of the winter 

2009 that probably will be remembered 

is the outbreak of the so-called “swine 

flu”. The new virus A-H1N1 captured 

the attention of the Italian media, 

which literally bombarded the 

population with daily reports on the 

number of deaths, the severity of this 

virus and other alarms based on the 

opinion of some “epidemiology 

experts”, spreading in this way the 

fear within the population. 

During the first weeks of autumn some 

sentences such as “We will have an 

extraordinary peak of flu diffusion between Christmas and 

the new year” or “we will be the victim of a new pandemia 

with many deaths” were pronounced. 

How is it possible to predict such an “apocalyptic” scenario 

so many weeks in advance? The truth is that it is extremely 

difficult, especially when no previous knowledge on the virus 

behavior is available. However, in epidemiology some simple 

mathematical models have been developed and used for 

many years; they are mainly based on ordinary differential 

equations (shortly ODEs). 

Probably, the most known model is the so-called SIR model, 

where the population, which is supposed to be large and 

homogeneous enough, is divided into three groups 

(Susceptible, Infected and Recovered), according to their 

status (see [4]). Strong simplifications are present in this 

model which can be applied as scale level; in some cases it 

could lead to poor results. For this reason, there is a variety 

of SIR based models which remove some of these 


A photo of the A-H1N1 virus (left) and a swine (right). They do not look so dangerous… 

Figure 2: The solution of a classical SIR model: the three categories S, I and R are plotted versus time, 

expressed in weeks. It is clear that the disease has a peak between the first and the second week and that the 

maximum number of ill people is around 150 over 1000. In this case we adopted the following values for the 

parameters (β=10, ν=5 and the number of initial infected is 1.98 for 1000 persons). 

simplifications in an attempt to be closer to reality. In this 

work, we suggest to add a new category to the standard SIR 

model in order to consider the fact that unfortunately, some 

infected people may die. The resulting model can be 

expressed as: 

This is a non-linear system of first 

order ODEs; the four categories used to 

classify the population are S = 

Susceptible, I = Infected, R = 

Recovered, D = Dead and they are 

expressed in percentage terms. For this 

reason the sum of all the categories 

has to be always equal to one. The 

parameters β, ν and δ are constants 

which determine the evolution of the disease. The results 

strongly depend on the numerical values of these parameters. 

Specifically the peak value of the infected and the week of 

the year when it will appear, which 

are important information to have 

in advance, can be really difficult to 

capture if there is not a rigorous 

estimation of the above mentioned 

parameters. 

Obviously, it is mandatory to know 

the initial conditions before solving 

the system: in other words we have 

to know the number of susceptible, 

infected, recovered and dead 

persons at time zero, when we want 

to begin our simulation. 

The solution of such equations is 

always done, excluding trivial cases, 

through numerical techniques which 

have been expressively defined to 

tackle this kind of problem. To


Table 1: The number of infected persons over 1000 (data source [2], 

November 15th) and the total deaths due to the swine flu (reported by the 

italian media) in Italy are reported in this table for some weeks of the year. 

obtain reliable solutions, the numerical strategies have to 

consider the nature of the ODE to be solved; in general, ODEs 

can be really complicated and strongly nonlinear and the 

independent functions could have sharp variations within 

time. 

For this reason, many techniques have been developed as it 

can be easily seen in literature (see [5] and [6] just to have 

an idea), to minimize the difference between the numerical 

and the theoretical solution. 

The implementation of such techniques in general is not an 

easy task for many engineers and scientists who probably are 

more interested in obtaining a reliable solution for their 

problems rather than in spending time and money in 

compiling codes. 

Figure 3: The modeFRONTIER workflow used for the model tuning problem. 

To partially mitigate this situation, we use a general-purpose 

and open source platform, Scilab (see [2]) which provides 

the user with powerful numerical tools to manage different 

problems and to solve an ODEs system. 

In Figure 2 the three categories S, I and R (these quantities 

are measured with reference to a population of 1000 persons) 

have been plotted versus time (expressed in weeks). In this 

case a classical SIR model has been solved: it can be easily 

seen that the number of infected persons 

amounts to a maximum of 150 out of 1000 and 

that it falls between the first and the second 

week. The model parameters (β=10, ν=5 and 

the number of initial infected is 1.98 for 1000 

persons) have been chosen in this case 

without any reference to a real disease. 

Unfortunately, the model parameters are not 

known in advance but, usually, they have to 

be estimated starting from some previously acquired 

knowledge on the evolution of the disease. Once these 

parameters have been estimated, it will be possible to 

predict the spread of the disease. 

This is a typical model tuning problem which could be 

formulated, for example, as a least square problem. Actually, 

if we knew the number of infected persons and the deaths 

which can be ascribed to the flu during a given period, we 

could try to find out the values for the model parameters in 

order to best fit the known data. The result could give a 

better insight into the flu evolution, and the possible 

predicting of the peak of the infection and hence a better 

understanding of the general evolution of the disease. 

To this aim we decided to use the data reported in Table 1, 

which are provided by the ISS (Istituto Superiore di Sanità) 

and collected by the author from different Italian media (see 

[2]). It is obvious that they are not numerous, but they are 

the only ones available at the end of the 46th week of the 

year (November 15th). However we would like to predict the 

swine flu evolution in Italy for the following weeks. 

As mentioned above, our aim is now to find out the best 

values of β, ν and δ in such a way that our modified SIR 

model is able to best fit the data reported in Table 1. We are 

building the modeFRONTIER project drawn in 

Figure 3: the four input variables are represented 

by the four green nodes in the upper part while 

the output variables, the number of the infected 

and the deaths at different weeks, are extracted 

directly from Scilab through two output vectors 

(the blue nodes). 

Among the many available strategies to adopt 

for the solution of this problem, we decided to 

use the following one, which has the desirable 

feature to lead to a mono-objective 

minimization problem. 

We introduce a target node, involving the 

computed number of infected people, looking 

for the best fit: 

and a constraint node, involving the number of actual 

deaths: 

For the solution of such a problem, usually, a Levenberg- 

Marquardt algorithm is recommended, in view of the nature 

Table 2: A comparison between the best solutions found with the two optimization algorithms 

adopted. It can be seen that the NLPQLP provides a better solution. C44, C45 and C46 represent 

the value of the constraint as defined in equation (2) expressed for the weeks 44, 45 and 46 

respectively.

Figure 4: The evolution of the swine flu in Italy according to our modified SIR model. Note that the logarithmic 

scale has been adopted for the persons ages. It can be seen that the flu peak falls between the 45th and 46th 

week (blue curve)and that it concerns about 13.5 persons out of 1000. The flu should practically disappear 

before the new year. The triangles represent the fitted data, contained in Table 1: it immediately becomes clear 

that the death rate is slightly overestimated. 

of the function to be minimized. It is well known, however, 

that this algorithm adopts a penalty oriented approach to 

manage constraints, which may not be the best in our case: 

we actually would like to have a very accurate fit also for the 

death rate, which is involved directly by the constraints. 

The NLPQLP algorithm, which has a completely different 

approach in the constraint management, has also been 

tested: it can be seen (from Table 2) that it provides better 

results than the Levenberg-Marquardt algorithm. 

The evolution of the flu is reported in Figure 4, as computed 

using the best fit parameters by NLPQLP. It is evident that 

the peak falls between the 45th and 46th week and it 

concerns about 13.5 persons out of 1000. Moreover, it points 

out that the flu should practically disappear before the new 

year. The mortality rate can be estimated by looking at the 

number of deaths after a long period (let us say after one 

year from the beginning): in our model this value amounts to 

0.0028 out of 1000 persons, which means 0.03% (170 deaths 

in Italy approximately). This value appears to be very close 

to analogous quantities computed for other 

seasonal flues in the past, which usually 

range between 0.02% and 0.04%. Finally, it 

has to be mentioned that the deaths are 

slightly overestimated in our model. 

However, if the reader visits the web site 

given in [2], he/she can read that the 

proposed data could be affected by slight 

variations, due to some delays in reporting by 

the surveillance network. Probably, the 

number of infected persons will not be exactly 

the same as those reported in Table 1, after 

November 15th. Hence knowing that the 

available data at the end of the 46th week 

may not be accurate, we would like to 

estimate how our previsions reported above, 

could change. In other words, we want to 

understand what is the error rate of our 


previsions, as the target data may 

be affected by slight variations. 

Here, the first step certainly is to 

give a probabilistic characterization 

of the target values; we decided to 

use an exponential probability 

density function for each target 

value of the infected persons. This 

choice has been driven by the fact 

that the true values of the infected 

persons are certainly higher than 

those reported in Table 1; actually, 

they are expected to grow. In Table 

3 the values of the location and the 

scale for the four exponential 

probability functions are collected. 

These values have been arbitrary 

chosen (there is no information on the reliability of data we 

have) in such a way that values lesser than those reported in 

the last column of Table 3 have around 90% of probability to 

appear. 

Five thousand simulations have been organized modifying 

the target values in accordance with the given probability 

density functions mentioned above and the corresponding 

peak position, peak intensity and mortality have been 

computed. 

Table 3: The location and the scale parameters of the exponential probability 

functions used to characterize the target values of the infected people at 

different weeks. 

Figure 5: The modeFRONTIER workflow used for the solution of the sensitivity problem. A Latin- 

Hypercube technique has been used to generate a DOE in accordance with the probability density 

functions characterizing the targets. In the project shown in Figure 3, the model tuning problem is 

solved by modeFRONTIER with a batch call and a Scilab routine which are used to continuously 

extract the information about the disease.


Figure 6: The peak is plotted versus the peak position. The bubble color is used to represent the mortality. 

The cloud of points summarizes the simulated scenarious. It can be easily seen that even the worst previsions 

in terms of peak and mortality rate have nothing in common with a pandemia or a catastrophic outbreak. 

To launch these simulations a new modeFRONTIER project has 

been organized (see Figure 5): the Latin-Hypercube 

algorithm has been set up to plan an appropriate DOE and a 

batch call to the project described before has been applied 

in order to solve the model tuning problem. A Scilab routine 

finally extracts the results in correspondence with the best 

solution found. 

In Figure 6 a bubble chart is shown: the peak value is plotted 

versus the peak position and the bubble color is used to 

represent the expected mortality. It can be easily seen that 

we obtain three ranges of existence; we can say that the 

peak position ranges between 45.29 and 45.60 weeks and 

that the peak ranges between 13.19 and 13.24 infected for 

one thousand inhabitants. The mortality rate never passes 

the 0.00347 over 1000 persons. It is interesting to observe 

that the resulting cloud of points is not uniformly nor 

homogeneously distributed, but it has important voids and 

regions with high densities. 

To understand how the probability of the couple peak and 

peak position is distributed, we have built the diagram 

plotted in Figure 7. The cloud of points has been divided in 

a 20 x 20 cells regular array, and we have counted the 

number of designs inside each cell. These counts have been 

divided by the total number of computed designs obtaining 

the relative frequency, which can be reasonably associated 

with the probability. This plot allows to say 

that peaks of around 13.35 infected falling 

between the 45.43 and 45.44 week are the 

most probable ones. We can conclude that, 

even if considering uncertainties in the 

target values, it is possible to estimate the 

spread of the disease with a reasonable 

accuracy: it is certainly possible to exclude 

catastrophic scenarious even if few data are 

available. During the next weeks we will see 

if the model presented in this work has 

been able to correctly predict the spread of 

the swine flu or, on the contrary, if a 

terrible outbreak will happen. Let's hope for 

the best and be optimistic! 

Conclusions 

In this work we have shown how it is 

possible to model the natural spread 

of a disease, within a population, 

with relatively simple equations. If 

some observed or measured data are 

available it is possible to tune the 

model and predict the evolution of 

the disease with sufficient accuracy 

at macro scale. 

The Scilab platform has been used to 

numerically solve the ODEs system 

and modeFRONTIER to tune the 

model and manage the uncertainties 

on available information. We would 

like to emphasize that the 

methodology adopted in this work can be used in the same 

way, also in other contexts, when a prevision is needed and 

experimental data are affected by errors. 

References 

[1] http://www.scilab.org/ to have more information on 

Scilab. 

[2] http://www.iss.it/iflu/ to have more information on the 

italian sentinel surveillance. The data relative to the 

infected have been downloaded here. 

[3] http://www.ministerosalute.it to have a complete 

description of the swine flu. 

[4] http://www.wikipedia.com to have more information on 

the SIR model. 

[5] P. Blanchard, R. L. Devaney, G. R. Hall, Differential 

Equations, (2006) Thomson Brooks/Cole, 3nd ed. 

[6] K. S. Bhamra, O. R. Bala, Ordinary Differential Equations. 

An Introductory Treatment with Applications, (2003) 

Allied Publishers PVT. LTD. 

For more information on this document please contact the 

author: Massimiliano Margonari - EnginSoft S.p.A. 


Figure 7: The frequency of the couples peak and peak position. The most probable scenarios are those 

characterized by a peak of around 13.35 infected falling around the 45.44 week of the year.

New trends in High Performance 

Computing 

New hardware and software technologies can reduce costs 

and computational time very effectively. In order to have 

productive clusters, the right choice of operating system, 

computer hardware, interconnection and disk storage is 

crucial. Moreover, also deployment and support for 

computational software installation must be taken into 

account in order to have cost-effective solutions which will 

not become a nightmare for users and administrators. 

Operationg system and queue system 

Two worlds: Linux with Perceus project and Microsoft HPC 

Server 2008 are the leading edge technologies for developing 

a cluster solution. 

Perceus 

Perceus is the next generation cluster and enterprise tool kit 

for the deployment, provisioning, and management of groups 

of servers. Employing the power of the Perceus OS and 

framework, the user can quickly suggest a machine out of the 

box. Perceus truly makes the computer a commodity, allowing 

an organization to manage large quantities of machines in a 

scalable fashion. 

Perceus is developed and provided to the world under the 

GNU GPL by Infiscale.com. 

HPC Server 2008 

Windows HPC Server 2008 provides a productive, costeffective, 

and high-performance computing (HPC) solution 

that runs on x64-bit hardware. Windows HPC Server 2008 can 

be deployed, managed, and extended using familiar tools and 

technologies. It enables broader adoption of HPC by 

providing a rich and integrated end-user 

experience, ranging from the desktop 

application to the clusters. A wide range of 

software vendors, in various verticals, have 

designed their applications to work 

seamlessly with Windows HPC Server 2008 

so that users can submit and monitor jobs 

from within familiar applications avoiding 

to learn new or complex user interfaces. 

The queue system: the heart of a cluster 

There are several points involved in a queue 

system: 

HOSTS 

Master host – The master host is central 

to the overall cluster activity. The 

master host runs the master daemon 

sge_qmaster. This daemon controls all 


Grid Engine system scheduling and components, such as 

queues and jobs. The daemon maintains tables about the 

status of the components, user access permissions, etc. 

By default, the master host is also an administration host 

and a submit host. 

Execution hosts – Execution hosts are systems allowed to 

execute jobs. Therefore, queue instances are attached to 

the execution hosts. Execution hosts run the execution 

daemon. 

Tipical High Performance cluster architecture 

When using the VGL Image Transport (formerly "Direct Mode"), the 3D rendering occurs on the 

application server, but the 2D rendering occurs on the client machine. 

VirtualGL compresses the rendered images from the 3D application and sends them as a video stream 

to the client, which decompresses and displays the video stream in real time.


Administration hosts – 

Administration hosts are hosts 

allowed to carry out any kind of 

administrative activity for the 

Grid system. 

Submit hosts – Submit hosts 

enable users to submit and 

control batch jobs only. In 

particular, a user who is logged 

in to a submit host can submit 

jobs with the qsub command, 

can monitor the job status with 

the qstat command. 

QUEUES 

A queue is a container for a class of 

jobs allowed to run on one or more 

hosts concurrently. A queue 

determines certain job attributes, 

for example, whether the job can be 

migrated. Throughout its lifetime, a 

running job is associated with its queue. The association 

with a queue affects some of the things that can happen to 

a job. For example, if a queue is suspended, all jobs 

associated with that queue are also suspended. Jobs do not 

need to be submitted directly to a queue. If you submit a job 

to a specified queue, the job is bound to this queue. As a 

result, the Grid Engine system daemons are unable to select 

a better-suited device or a device that has a lighter load. 

You only need to specify the requirement profile of the job. 

A profile might include requirements such as memory, 

operating system, available software, and so forth. The Grid 

Engine software automatically dispatches the job to a 

suitable queue and a suitable host with a light execution 

load. 

A queue can reside on a single host or can extend among 

multiple hosts. For this reason, Grid Engine system queues 

are also referred to as cluster queues. Cluster queues enable 

users and administrators to work with a cluster of execution 

hosts by means of a single queue 

configuration. Each host that is 

attached to a cluster queue 

receives its own queue instance 

from the cluster queue. 

License management 

Most commercial software use 

FLEXLM (tm) license management 

system to distribute licenses. The 

combination of licensing system 

with queue system has become in 

the past months a serious matter 

for mass intensive optimization 

computation, as well for users 

and system administrators. 

Available licenses are checked in 

only when the job has already 

entered the queue system, thus at 

that point is too late to deny a 

license because of no more licenses 

available. 

This is very disappointing for users 

coming back from weekend to find 

their optimization job basically not 

done over time, just because some 

other batch jobs where launced by 

other departments, or because 

network delays. The control of this 

situation needs a very deep 

understanding how queue systems 

work and interactions between all 

system components: customization 

must be well engineered to avoid 

interferences between the license 

manager and the cluster. 

We develop lots of custom scripts for SunGridEngine (fully 

platform independent, portable to Microsoft cluster system) 

to solve this problem and to make queue jobs start at right 

time, allocating the right licenses and sub-licenses. 

There will be a 0.1% of cases where this procedures will not 

work, spawning job at the wrong time, but this is a side 

effect of communication among daemons (queue, 

system,cluster etc..) that could not be taken away. 

Parallel applications 

The development of parallel programs requires integrated 

development environments along with the support for 

distributed computing standards. Visual Studio 2008 provides 

a comprehensive parallel programming environment for 

Windows HPC Server 2008. Besides supporting OpenMP, MPI, 

and Web Services, Windows HPC Server 2008 also supports 

third-party numerical library providers, performance 

optimizers, compilers, and a native parallel debugger for 

developing and troubleshooting parallel programs.

Large model with non-linear material and deformations example solved on a 

64 nodes cluster system 

Common bottleneck sources 

As the CAE industry continues an aggressive platform 

migration from proprietary Unix servers to commodity HPC 

clusters, CAE models are becoming more realistic, too, 

requiring clusters to handle ever-increasing volumes of I/O 

and the movement of large files. 

As organizations rapidly expand their cluster deployments, 

many encounter I/O bottlenecks when using legacy network 

attached storage (NAS) architectures. 

Initially, these NAS systems offered advantages such as shared 

storage and simplified IT administration which reduced costs, 

but today a few of them provide the scalability required for 

effective I/O performance in parallel CAE simulations. 

Recently, a new class of shared parallel storage technology has 

developed to remove serial bottlenecks and to improve i/o 

performances, therefore extending the overall scalability of 

CAE simulations on clusters. 

Parallel storage is the leading solution of parallel 

NAS and enables the most advanced and I/O 

demanding CAE challenges to become practical 

applications. Some examples include the highfidelity 

transient CFD, large eddy simulation 

(LES), aerocoustics, large DOF structural dynamic 

response, parameterized non-deterministic CAE 

simulations for design optimization and the 

coupling of CAE disciplines such as fluid-structure 

interaction (FSI). CAE workflows are 

overburdened with lost productivity when 

engineers and scientists must wait for serial I/O 

operations and large file transfers to complete. 

Furthermore, as simulation and workflow 

performance degrades, so does CAE analyst 

efficiency and effective workgroup collaboration. 

A parallel storage eliminates the I/O bottlenecks 

with a cost-saving solution that restores 

productivity and drives analyst creativity. 

The benefits of parallel I/O for transient CFD were 

demonstrated with a production case of an ANSYS 

aerodynamics model of 111M cells, provided by 


an industrial truck vehicle manufacturer. Figure 2 below, 

illustrates the I/O schematic of the performance tests that 

were conducted, which comprised a case file read, a compute 

solve of 5 time steps with 100 iterations and a write of the 

data file. In a full transient simulation the solve and write 

tasks would be repeated to a much larger number of time steps 

and iterations, and with roughly the same amount of 

computational work for each of these repeatable tasks. 

It is important to note that the performance of CFD solvers 

and the numerical operations are not affected by the choice of 

the file system, which only performs I/O operations. That is, a 

CFD solver will perform the same on a given cluster regardless 

of whether a parallel or serial NFS file system is used. The 

advantage of parallel I/O is best illustrated in a comparison of 

the computational profiles of each scheme. ANSYS CFD 12 on 

PanFS keeps the I/O percent of the total job time in the range 

of 3% at 64 cores to 8% at 256 cores, whereas 6.3 and NFS 

spend as much as 50% of the total job time in I/O. 

Visualization and Postprocessing 

Another relevant matter of large cluster is visualization and 

post-processing of results on relatively slow networks. An 

effective solution is performing 3D renders with openGL inside 

the cluster and giving the client the possibility of remote 

Display. 

VirtualGL is an open source package which gives any Unix or 

Linux remote display software the ability to run OpenGL 

applications with full 3D hardware acceleration. Some remote 

display software, such as VNC, lacks the ability to run OpenGL 

applications at all. 

Tipical cluster management system and visualization nodes


Other remote display software forces OpenGL applications to 

use a slow software-only OpenGL renderer, to the detriment of 

performance as well as compatibility. The traditional method 

of displaying OpenGL applications to a remote X server 

(indirect rendering) supports a 3D hardware acceleration, but 

this approach causes all of the OpenGL commands and 3D data 

to be sent over the network to be rendered on the client 

machine. This is not a tenable proposition unless the data is 

relatively small and static, unless the network is very fast and 

unless the OpenGL application is specifically tuned for a 

remote X-Windows environment. 

With VirtualGL the OpenGL commands and 3D data are instead 

redirected to a 3D graphics accelerator on the application 

server and only the rendered 3D images are sent to the client 

machine. Thus VirtualGL "virtualizes" 3D graphics hardware 

allowing it to be placed in the "cold room" with compute and 

storage resources. VirtualGL also allows the 3D graphics 

hardware to be shared among multiple users and provides 

"workstation-like" levels of performance even on the most 

modest of networks. This makes it possible for large, noisy, hot 

3D workstations to be replaced with laptops or even thinner 

clients. More importantly, however, it is the fact that VirtualGL 

eliminates the workstation and the network as barriers to the 

data size. Users can now visualize gigabytes and gigabytes of 

data in real time without needing to copy any of the data over 

the network or sitting in front of the machine that is rendering 

the data. 

Usually, a Unix OpenGL application would send all of its 

drawing commands and data, both 2D and 3D, to an X- 

Windows server which may be located across the network from 

the application server. VirtualGL, however, employs a 

technique called "split rendering" to force the 3D commands 

from the application to go to a 3D graphics card in the 

application server. VGL performs this by pre-loading a dynamic 

shared object (DSO) into the application at run time. This DSO 

intercepts a handful of GLX, OpenGL, and X11 commands that 

are necessary to perform the split rendering. Whenever a 

window is created by the application, VirtualGL creates a 

corresponding 3D pixel buffer ("Pbuffer") on a 3D graphics 

card in the application server. 

Whenever the application requests that an OpenGL rendering 

context have to be created for the window, VirtualGL 

intercepts the request and creates the context on the 

corresponding Pbuffer instead. Whenever the application 

swaps or flushes the drawing buffer to indicate that it has 

finished rendering a frame VirtualGL reads back the Pbuffer 

and sends the rendered 3D image to the client. 

For further information: 

Ing. Gino Perna - ICT Manager 


An example of a mesh generation for a reactor pressure vessel, 11 million nodes and 35 million DOFs. 

Enginsoft provides all ranges of HPC solutions: from ready 

to use systems to dedicated HPC setup for specific needs 

in the simulation market. 

Enginsoft expertize ranges from system configuration, 

queue control, monitoring tools, licensing integration and 

etherogeneous systems building to maintain cluster 

efficiency along time. 

Also integration with parallel file systems and remote 

graphic system is under continuous monitoring to provide 

our customers with the best of class solutions.


Development of Digital Mechatronic 

Applications using Co-Simulation 

The ever decreasing size and cost of embedded 

microcontrollers have brought digital electronic equipment 

to be used in almost every physical process or machine. In 

the real world, the software that runs on the microcontrollers 

actually implements the logic, the decision making and the 

control functionality of industrial processes, transportation 

SimNumerica s.r.l. and his FE partner 

EnginSoft s.r.l. outline their approach to the 

virtual prototyping of systems where an 

embedded microcontroller controls a 

multiphysical process or a machine. 

systems, machine tools and electrical appliances in general. 

Moreover, the availability of low-cost sensors and actuators 

that provide a multitude of physical quantities in various 

fields to the electrical pins of a microcontroller, has made 

embedded electronics a crosswise pervasive ingredient to 

many engineering applications. 

In this context, a computer program (usually written in C 

language) becomes effectively a component of the 

engineering application. Therefore, it must be designed, 

optimized and verified like any other physical component. 

Moreover, these engineering steps have to be performed 

taking into account also the fine scale interactions that this 

running software develops with the physical components. In 

fact, the validation of a system governed by microcontrollers 

cannot be approached without taking into consideration the 

embedded control firmware and, on the other hand, the 

validation of the firmware cannot be performed without 

considering its embedding physical system. 

Therefore, the development of a digital mechatronics 

application is a tricky mixture of physical and abstract 

phenomena, since the physics of the software execution are 

mostly unobservable in a physical experiment. In a computer 

simulation, instead, the execution of the microcontroller 

software can be replicated exactly. Moreover, by adding a 

model for the simulation of the physical system (such as 

those commonly used in the FE-based design), a detailed 

evaluation of the interaction between the embedded software 

and the physical system becomes possible. 

SimNumerica s.r.l. is targeted at the exploitation of muLab, 

the Microcontrolled Systems Simulation Laboratory, a 

prototype of which has been developed and widely tested at 

the University of Padua by a team of experts in numerical 

mathematics, electronics and software. muLab has been 

tested in a variety of pilot projects, which have already 

clearly demonstrated the advantages offered by muLab 

compared to general purpose platforms for the development 

of numerical algorithms and the hardware-in-the-loop 

approach. 

muLab performs the co-simulation of the embedded software 

directly in the binary format which is executable by the 

microcontroller. In this way, the production software can be 

designed and tested well before the hardware prototype is 

available. Moreover, when the final product is available, a 

much larger set of functional tests can be performed in the 

co-simulation model, with respect to those feasible in a 

physical laboratory. 

With muLab, firmware people and mechanical engineers 

become aware of their mutual responsibilities concerning the 

final performance of their design activity. This is important 

since, in principle, software components are not 

understandable to mechanical engineers and, vice versa, 

electronics engineers often are not adequately familiar with 

mechanical components. 

FEA and muLab 

Finite Element Analysis and the co-simulation implemented 

in muLab have the same DNA in common: they reveal the 

details of physical phenomena occurring at various space and 

time scales. In this way, they allow to observe the 

interactions between a software running on a microcontroller 

and its embedding physical system, with an approximation 

level decided by the user. 

In the same digital mechatronics application, the time-scales 

involved may be quite distant from each other, e.g. firmware 

instructions are executed in microseconds or less, digital 

electronics signals present a milliseconds time base, 

kinematic/dynamic variables evolve in centesimal fractions of 

seconds and thermal variables evolve in several seconds. 

For this reason, we use the term Computational Digital 

Mechatronics when we refer to this type of co-simulation. It 

inherits all the numerical engineering aspects of 

computational mechanics, plus: 

the co-simulation of a multiphysical engineering system 

and of the digital embedded hardware/software that 

interacts with this system; 

the numerical analysis of the algorithms implemented in 

the embedded microcontroller software (firmware), that 

runs within the numerical simulation model of the whole 

system.


muLab is a fundamental tool for a large variety of 

applications designed with FEA. Indeed, even in simple 

mechatronics applications, such as the temperature control 

of an air-conditioned railroad car (Figure 1), the algorithmic 

functionality implemented in a microcontroller may be quite 

complex. In general, it has to: 

read the temperature sensors and filter/compensate 

electrical disturbances and physical deficiencies of the 

sensor (nonlinearities, thermal inertia, condensed vapour, 

etc); 

infer the temperature at the user site; this is usually an 

indirect measurement, since the sensor can only be 

placed in hidden locations, which is performed by an 

algorithm that uses a numerical model of the process to 

predict the unknown quantity; the numerical model must 

typically have low computational cost and it is built by 

using emerging numerical methods in engineering, such 

as model order reduction, system identification, machine 

learning. 

implement the logical behaviour required by the machine 

design; this is usually a rather complex set of functions 

and procedures that covers machine initialization and 

configuration, manual or self-diagnosis, different 

operational modes, failure recognition and safe reaction, 

etc. 

Figure 1 

control the actuators according to the specifications; this 

usually involves algorithms to safely operate, optimize 

and monitor the physical actions performed by actuators 

and related subsystems. 

These algorithmic functionalities are easy to implement and 

verify in muLab, especially when they require the support of 

a numerical model. In particular, system identification and 

machine learning, which are based on the comparison 

between model predictions and experimental data, will be 

algorithmically supported explicitly in the near future. 

MuLab: the software tool 

The main feature of the software tool muLab is the 

simulation-based prototyping and validation of algorithms 

that should run on the microcontrollers embedded in a 

variety of digital mechatronics systems. The approach 

followed by muLab is hardware software co-design. 

A main ingredient is the ability to monitor the functional 

Figure 2 

behaviour of the system up to the finest scale detail. To do 

this efficiently, muLab offers to the user the possibility to 

write a multi-level ensemble of debug procedures (Figure 2) 

that renders this monitor activity fully automatic during the 

simulation. This is very important because the user typically 

wants the computer to do hundreds of simulations during the 

night. The language used to write the debug procedures is a 

slight customization of the simplest programming languages 

actually used in computer programming. 

Moreover, muLab is a collaborative design tool: the 

development of physical models becomes visible to 

the firmware designer and the firmware behaviour 

becomes visible to the mechanical engineer (Figure 

3). As a consequence, the firmware development 

takes place in parallel with the hardware 

construction and fits to it. 

At the same time, the physical system structure and 

organization can be cheaply modified until the 

expected performance appears to be adequate. 

The environment includes also a source code 

debugger (Figure 4) that works both for the 

numerical models of the physical components and for the 

embedded software (firmware). The possibility to set a 

breakpoint during the simulation of a mechatronic system 

allows a deep understanding of the interactions between the 

firmware and its embedding physical system. 

It allows the numerical analysis of algorithms that run on 

embedded microcontrollers, i.e. running in a non-sequential 

mode. This is usually much more difficult than it is for FE 

numerical methods. In fact, their execution is intrinsically 

non-sequential and may actually involve several subtasks 

executed by routines which are activated by interrupts 

caused by non-deterministic (and sometimes only loosely 

predictable) events. 

Last but not least, muLab uses Standard and open languages 

and data formats: 

component model equations may be written in Python. 

model structures and user procedures are coded in XML.

Figure 3 

Advantages: reduced experimentation costs and 

development time 

muLab enhances firmware debugging and simulation-based 

robust design. This is accomplished through the specification 

of detailed Test Sequences (Figure 5) by which the user can: 

specify complex test sequences; 

track and identify firmware fault conditions; 

Implement failure mode analysis (FMEA) of the hardware 

components. 

The automation of test sequences allows the user to verify 

the application functionality in a large variety of situations, 

much larger than what is physically possible. Moreover, 

the experimental test phase can take place selectively 

and on a relatively mature firmware, where a large number 

of bugs has already been removed; 

the quality and value of the firmware verification 

procedure increases, while the debugging time decreases. 

Thus, time, energy and money can be saved. 

Practical issues concerning the multi-level debugging of the 

firmware: 

About SimNumerica and EnginSoft 

SimNumerica was founded by a dedicated research team, all 

experts with broad experiences in numerical mathematics, 

electronics and software design, of University of Padua – 

Italy. SimNumerca’s industrial partner and co-founder 

EnginSoft is an international CAE Computer-Aided 

Engineering Consulting company with unique 

multidisciplinary competencies in virtual prototyping. 

SimNumerica’s joint expertise is focused on environments for 

the virtual prototyping of mechatronics systems based on 

micro-controllers. 


one advantage of the numerical simulation compared to a 

corresponding physical experiment is that the former is 

deterministic, and hence repeatable, while the latter is 

not; 

the methodology implemented in muLab supports a userdefined 

ensemble of debug procedures that monitor the 

numerical simulation: if something is suspect, a debug 

procedure can restart the simulation with increasing 

levels of diagnosis. In this way, following the diagnostic 

tree, the details of a wrong behaviour of the system can 

be traced at affordable time and computational cost. 

Future Development 

In a future release, additional parallel computing capabilities 

will be integrated in the software package. In particular, 

multi-core platforms and graphical processors (GPGPU) will 

be supported. The target is an efficient co-simulation of 

computational intensive models, such as large-scale dynamic 

FEM models, and of large firmware codes, in particular the 

ones involving the control of processes whose duration 

extends to relatively large time-scales. The combined use of 

multi-core CPUs and GPUs makes computational digital 

mechatronics affordable to small industrial engineering 

teams, even for quite complex applications. 

Contact 

Fabio Marcuzzi, PhD - Simone Buso, PhD 

SimNumerica s.r.l., Pordenone - Italy 

email: info@simnumerica.it 

Figure 4 

Figure 5


Innovation and EnginSoft 

in the USA 

SUNNYVALE, California – December 10, 2009 - Stefano 

Odorizzi, CEO of EnginSoft, has recently returned from a trip 

to the USA and has reported that recruitment, sales, and 

expansion plans are moving forward rapidly. 

EnginSoft CEO strengths connections with the US market 

EnginSoft is continuously strengthening its connections and 

network in the US market. Stefano Odorizzi visited the United 

States in the last week of October to view and develop 

EnginSoft's local initiatives further. He met and interacted 

with several existing clients, and thus gained insights into 

how EnginSoft's services have benefited their business 

models. 

During his visit, Stefano wanted to discuss with US customers 

their visions for future product development, their concerns 

and existing alternatives to develop efficient working 

methodologies between clients and the EnginSoft teams. He 

managed to share EnginSoft's philosophy and business model 

with all the people he met during his short visit, and stated, 

"I am pleased to report that our initiatives in California are 

moving forward and that there has been an incredible 

amount of interest and enthusiasm shown by the local 

market. I had the pleasure of reviewing our operations and 

meeting with our management staff to discuss the next 

phases of our operations”. 

During this trip, Stefano met with some of the leading 

experts in the areas of Electronic Design Automation (EDA), 

Computational Fluid Dynamics (CFD), and Design 

Optimization. Stefano emphasized the positive results which 

could be achieved to date in the American market despite the 

tough economic situation. He visited some of the world's top 

academic institutions, namely University of California at 

Berkeley, University of Stanford, and the University of Santa 

Clara. These relations will support the company to further 

develop its future technologies and strategies. Stefano's visit 

was very successful and is a milestone inEnginSoft's road 

map. The outcomes will be incorporated in the company’s 

partners' network and business sectors. 

EnginSoft at UC Berkeley - Prof. Stefano Odorizzi meets 

with Prof. Alberto Sangiovanni Vincentelli 

During his visit at the University of California at Berkeley, the 

EnginSoft CEO met with Professor Alberto Sangiovanni 

Vincentelli, a worldwide renowned expert and cofounder of 

Cadence and Synopsys. 

The talk was a starting point for interaction and exchange of 

CAE, EDA, and VP knowledge, development and application 

results. The two experts shared 

their visions for the future of 

engineering simulation in 

industry with a very positive 

outlook for the upcoming 

years. With its highly 

innovative engineering and 

technology organization and network, EnginSoft wants to 

become an important player in the Global Computer Aided 

Engineering market fueling its growth also through close 

collaborations with top academic institutions. 

Prof. Alberto Sangiovanni Vincentelli 

holds the Edgar L. and Harold H. Buttner 

Chair of Electrical Engineering and 

Computer Sciences at the University of 

California at Berkeley. Moreover, he is a 

co-founder of Cadence and Synopsys, 

the two leading companies in the area 

of Electronic Design Automation. He is 

the Chief Technology Adviser of Cadence, and a member of 

the Board of Directors of Cadence and the Chair of its 

Technology Committee, UPEK. 

The University of California at Berkeley and its 

flagship campus were founded in 1868. 

Berkeley ranks first nationally in the number 

of graduate programs in their espective fields. 

Among its active faculty are 7 Nobel 

Laureates, 28 MacArthur Fellows, and 4 Pulitzer Prize 

winners. Today it is the world's premier public university and 

a wellspring of innovation. 

EnginSoft special guest at the Business Association Italy 

America - BAIA 

In this successful networking event organized by BAIA, Prof. 

Stefano Odorizzi presented and discussed the capabilities of

leading edge technologies used to provide improved 

engineering designs across various innovative applications. 

Stefano shared the key factors that have led to his success as 

an Italian entrepreneur building a US and Global business, as 

well as the crucial role played by global partnerships with 

both companies and universities. This talk turned out to be 

a great opportunity to mix and mingle with young and 

seasoned professionals, entrepreneurs, students, and all the 

extended BAIA community, while enjoying Italian wine and 

delicious appetizers. 

Stefano participated in an animated and dynamic roundtable 

discussion with students of the Fulbright BEST group 

pursuing the Certificate of the Technology Entrepreneurship 

program offered by the Center for Innovation & 

Entrepreneurship (CIE) of Santa Clara. 

BAIA is an independent, 

nonprofit, open, apolitical 

business network that offers a 

place (physical and virtual) to 

facilitate the open exchange of 

knowledge and information, business opportunities, 

relationships and to promote a culture of innovation through 

entrepreneurial spirit and principles for Entrepreneurs, 

managers, professionals and interested individuals in the 

United States and in Italy. For more information, please visit 

http://www.baia-network.org/ 

EnginSoft at Stanford - Several Points in Common 

Stefano also met with Prof. Gianluca Iaccarino of Stanford 

University. The meeting turned out to be a pleasant talk 

between two people who share an enthusiasm for innovation 

and excellence. EnginSoft and Stanford both act as 

laboratories for technology transfer to industry. They strongly 

invest in the next generation of engineering and technology 

experts, to foster their growth and dynamism, a perfect 

combination for a future collaboration. The obvious synergy 

between Stanford and EnginSoft will provide our customers 

with even more innovative solutions to meet industrial 

challenges, such as increasing quality and reducing project 

times. 

EnginSoft provide access to a range of services related to the 

calculation and optimization of thermo-fluids, unmatched to 


date by any other European or American CAE company. 

Prof. Iaccarino and Prof. Odorizzi discovered that they have 

a lot of interests and business objectives in common. A part 

from the high technological content of their meeting and 

talks, both are aware that great networking opportunities are 

indispensable to realize innovative visions. 

Please stay tuned for upcoming news in the Newsletter 

Editions of 2010 and on www.enginsoft.com 

Stefano Odorizzi also met with Stanford Professor Bernard 

Widrow. In fact, the first statement of Professor Widrow 

was: “Optimization is everywhere," certainly a great start 

for a sparkling conversation! The two gurus exchanged 

ideas about the use of optimization techniques applied to 

human-like memory computers while enjoying tea. Prof. 

Widrow underlined its outstanding ability and talent for 

describing his most complex research at Stanford with simple 

words. 

Silicon Valley represents a unique blend of knowledge, 

advanced research, remarkable 

capital investments, and expertise. All this makes Silicon 

Valley an ideal and unique place to do business. 

Prof. Gianluca Iaccarino is an Assistant 

Professor at the Mechanical Engineering 

Institute for Computational Mathematical 

Engineering at Stanford University with 

many years of experience in fluid dynamics, 

physical modeling and advanced computer 

simulations. 

Prof. Bernard Widrow's 

research at Stanford 

focuses on adaptive 

signal processing, 

adaptive control systems, 

adaptive neural networks, 

human memory, and 

human-like memory for 

computers. He is the 

coinventor of the Widrow-Hoff Least mean squares filter 

(LMS) adaptive algorithm with the doctoral student Ted Hoff. 

The LMS algorithm led to the ADALINE and MADALINE 

artificial neural networks and to the back propagation 

technique. He has more than 21 patents under his name. 

Stanford University is located between San Francisco and San 

Jose, in the heart of Silicon Valley, it is world-known for its 

multidisciplinary research within its schools and 

departments, as well as its independent laboratories, centers 

and institutes. There are currently more than 4,500 externally 

sponsored projects throughout the university, 

with a total budget for sponsored 

projects of $1.060 billion during 

2008-09, including the SLAC 

National Linear Laboratory 

(SLAC).


BENIMPACT 

Building’s ENvironmental IMPACT 

evaluator & optimizer 

BENIMPACT is a research project co-funded by the autonomous 

Province of Trento (Northern Italy) by means of the ERDF 

(European Regional Development Fund), whose priorities 

include research, innovation, environmental protection and 

risk prevention. The duration of the research activities is 

foreseen to be a couple of years. 

BENIMPACT mainly aims at the development of methodologies 

(and of a related prototypical software platform) to support 

architects and engineers in the design of eco-sustainable 

buildings. The methodology shall be used to optimize the 

design of green buildings and will allow to identify the 

optimal trade-off between costs and environmental 

performances of the buildings. 

The research activities will be carried out by EnginSoft and a 

bunch of authoritative partners: the Department of Civil and 

Environmental Engineering of the University of Trento, 

DTTNhabitech and the Trentino Institute for Social Housing. 

EnginSoft has carefully chosen the partners on the basis of 

their specific knowledge and their contribution to the research 

activities: the University of Trento will share its long-time 

experiences related to energy modeling tools and 

methodologies; DTTN-habitech, a consortium of more than 300 

companies operating in the green-building trade, will supply 

the required linkage with the market and their deep knowledge 

in green building rating tools (such as LEED, Green Star, 

BREEAM, etc.). The Trentino Institute for Social Housing, the 

public organization of the Province of Trento that manages 

and develops public residential housing projects, will bring to 

the project group also the views of today's 

building designers. 

The design methodology which will be defined 

during BENIMPACT, will allow to carry out an 

integrated building design process: all the steps 

required to achieve the design of a green building 

will be contemporary realized by means 

of a bunch of analysis tools reciprocally 

integrated into each other. This approach will 

significantly innovate traditional ones: in fact, 

nowadays, 

each different building design topic is 

completed independently by different 

professionals and 

in different design stages, thus leading to the 

definition of the building design parameters by 

means of subsequent steps and to buildings that are not 

optimal in relation to all the required objectives. 

However, now with BENIMPACT, the use of the advanced 

modeFRONTIER technology will allow to build a suite of 

integrated applications (the BENIMPACT suite), that interact 

in a typical design chain process. Figure 1 shows the foreseen 

architecture of the design suite that will be implemented (in 

a prototypical release) in BENIMPACT. The shown architecture 

foresees the cooperation of core modules, databases and 

service modules (green design solutions and targets setting 

modules). 

In particular, the core modules are the embedded applications 

of the suite in charge of carrying out the whole calculations 

and analyses. The multi-objective optimization module is 

actually the kernel of the whole suite: thanks to the 

modeFRONTIER functionalities, it will lead the search for the 

optimal design features of the building, supplying a design 

environment where all the other applications are integrated. 

The geometric modeling module will translate the geometry of 

the building into a parametric model, able to store 

information related both to the building shape and materials, 

components and systems that will constitute the building 

construction. The energy modeling module will evaluate the 

energetic consumption of the designed building, taking into 

account the thermal loads required in order to guarantee 

predefined indoor comfort levels. The LCA (Life Cycle 

Assessment) modeling module will calculate the environmental

impact of the building during its entire life, and hence also 

consider the impacts that arise from resources extraction, 

manufacturing, on-site construction, occupancy/maintenance, 

demolition and recycling/reuse/disposal. The LCC (Life Cycle 

Costing) modeling module will evaluate the costs of the 

building, totaling up in a unique value the complete costs for 

the building construction, its maintenance, occupancy (i.e. the 

costs for energy consumption) and demolition. 

It is important to note that all the core modules will be software 

applications (customized or implemented by means of ad-hoc 

written codes) that will work also in a stand-alone modality in 

order to allow the validation of each single application. 

Furthermore, it is possible that, for some particular applications, 

such as energy modeling, some existing codes will offer the 

required functionalities: In such cases, freeware tools and opensource 

codes will be preferred to commercial ones, in order to 

allow further implementations and improvements by the whole 

community of practice. 

The databases included in the BENIMPACT suite will supply the 

required input data to all the applications involved. They will 

store data related to the systems and components of the 

building constructions and to the energy production systems 

normally used in buildings (such as boilers, solar and 

photovoltaic panels, geothermal heat exchangers, etc.). They 

will also supply technical constraints, derived by the current 

building laws, and the required meteorological data. 

The service modules' green design solutions and target settings 

will supply border information to the analysis: the former, 

operating as an expert system, will suggest solutions for the 

green building design (such as green roofs, natural ventilation, 

externally ventilated façade, etc.), while the latter will translate, 

in an engineering format, the design objectives set by the user 

(for example, the design objective of low energy consumption 

should be translated, for the sake of the numerical analysis, into 

a defined value of Watts required per square meter of the 

building). 

EnginSoft strongly believes that the activities which will be 

carried out in the framework of the BENIMPACT research project 

will bring benefits to the building trade, thanks to the tuning of 

brand-new CAE tools that will eventually improve the green 

building design. Moreover, BENIMPACT will help to protect the 

environment, it will allow and promote the diffusion of ecosustainable 

buildings based on the definition, during the design 

phase, of the building features that guarantee lowenvironmental 

impacts and, at the same time, low life-cycle 

costs of the building. Last but not least, EnginSoft itself will 

take advantage of the BENIMPACT research activities: enhancing 

its knowledge related to green building techniques, the company 

will be able to expand its market to this emerging sector, 

offering its engineering consultancy services, specific software 

and educational program to new clients in in the eco-sustainable 

business, that seems to be thriving and not suffer from 

economic downturns. 

For futher information, please contact: 

Ing. Angelo Messina - R&D Manager 



EnginSoft al METEF2010 

Anche quest’anno EnginSoft 

prenderà parte alla Metef- 

Foundeq, l’ottava edizione 

dell’expo internazionale di riferimento dell’alluminio e dei 

metalli tecnologici in parallelo a Foundeq Europe expo 

internazionale degli impianti, attrezzature e prodotti della 

fonderia metalli. La manifestazione con cadenza biennale, a cui 

EnginSoft ha sempre partecipato, è in programma al Centro 

Fiera del Garda, a Montichiari, in provincia di Brescia, dal 14 al 

17 aprile 2010. 

EnginSoft presenzierà all’evento con uno proprio stand 

all’interno dell’area fieristica. In questa edizione verranno 

presentate le nuove releases di MAGMA, FORGE e ADVANTEDGE. 

Nella scorsa edizione, nel 2008, la fiera ha registrato la 

presenza di 568 aziende espositrici (di cui 396 italiane e 172 

estere), inoltre la cifra dei visitatori, provenienti sia dall’Italia 

che dall’estero, ha raggiunto quasi quota 19000. 

Si preannuncia quindi un evento di grande successo, arricchito 

da interessanti appuntamenti quali: 

una tavola rotonda a cura del Comitato Laminazione nella 

prima giornata denominata: “Il futuro della laminazione 

dopo la crisi: l'innovazione come chiave per vincere le sfide 

del mercato”, 

la presentazione del nuovo "Manuale di difettologia" 

intitolato “Difettologia dei presso colati” organizzato da 

AIM – Associazione Italiana di Metallurgia, Centro Studi 

Pressocolata 

la presentazione dei risultati del progetto europeo NADIA 

"New Automotive components Designed for a manufactured 

by Intelligent processing of light Alloys" coordinato da 

EnginSoft 

una conferenza internazionale organizzata da ITmetal.it e 

da CSMT Centro Servizi Multisettoriale e Tecnologico 

intitolata “ICT e settore dell’alluminio: quali formule per il 

successo”. 

Sito della manifestazione: www.metef.com


Continuing Higher Education on CAE: 

The TCN Consortium 

Born in 2001, TCN Consortium is a private Italian company 

which organizes higher education activities in the 

engineering and CAE (Computer-Aided Engineering) fields. 

The specific objective of TCN is to train the key resources, 

that ensure competitiveness to companies in each 

technological sectors fundamental to process and product 

innovation. The TCN motto – “training innovation leader” - 

embodies this objective. 

The TCN Consortium has been working for many years in a 

professional and reliable way; moreover, it supports the 

entrepreneurship and the training managers in projecting 

and distributing customized training trails. The TCN efficient 

and agile approach is based on surveying 

enterprises’ real educational needs to be 

converted into customized training trails: 

TCN Faculty, teamed by professors in 

Italian and foreign Universities together 

with experienced researchers and 

engineers, represents the key to achieve 

this goal. 

TCN helps the enterprises to face the 

innovation challenges, enabling them to 

face an ever-changing industrial and 

technological landscape, transferring the 

necessary knowledge to create highly qualified human 

resources, that will immediately work in the industrial 

environment. 

TCN Consortium offers courses from the catalogue and ondemand 

courses for entrepreneurship. In particular, TCN 

offers: 

Short Courses (1 to 5 days) 

MiniMaster (intensive training over two non-consecutive 

weeks) 

On-demand training via the Internet 

Publishing of manuals for the industry (TCN SBE&S Series) 

TCN maintains a strong European and international identity: 

every two years it organizes “TCN CAE International 

Conference on Simulation Based 

Engineering and Sciences”, an 

international conference based on the 

CAE technologies in the industry. 

Furthermore, TCN takes actively part in 

Europe to pilot projects aimed at 

designing innovative higher education 

contents and training trails for the 

industry. 

On the website www.consorziotcn.it it is 

possible to consult the up-to-date TCN 

courses catalogue for the 2010, which is 

constantly enlarged and updated on the entrepreneurs 

demand. Nevertheless, this is just a general view of the TCN 

training offer. 

For further information and requests, please contact: 

TCN Consortium organizing secretary Mirella Prestini 

Ph. +39 035 368711 – info@consorziotcn.it


Analizzare cinematica e dinamica dei 

meccanismi con le tecniche multibody: 

terminologia, ambiti di applicazione ed 

opportunità Le scuole di progettazione 

più tradizionali portano a 

considerare con maggior 

frequenza problematiche di 

tipo strutturale (incluse fatica 

ed acustica), fluidodinamico, 

o di processo. Gli 

strumenti di simulazione 

numerica offrono, in tutti 

questi ambiti, un aiuto formidabile 

ed efficace, ben 

noto alla grande maggioranza 

degli utenti che hanno 

una cultura ingegneristi- 

Automobile di Leonardo da Vinci 

(Codice Atlantico, f. 812r del 1478) 

ca moderna orientata all’efficienza. 

La progettazione meccanica è tuttavia un contesto dove possono 

diventare decisivi i fattori non analizzabili dalle suddette 

branche della simulazione. Si pensi, per esempio, alle caratteristiche 

di guidabilità di un veicolo, alla stabilità di 

una lavatrice, alla precisione di un cambio da bicicletta, per 

restare su applicazioni che riguardano la quotidianità. Allo 

stesso modo potremmo citare la velocità delle macchine per 

la produzione e la lavorazione su larga scala di qualsiasi prodotto 

“consumer” (per esempio tessile, carta, alimentari, semilavorati), 

senza dimenticare i complessi sincronismi nascosti 

all’interno di qualsiasi mezzo di trasporto (per esempio 

auto, treni, aerei). 

Tutte queste applicazioni sono accomunate da requisiti e 

prestazioni che non sono esclusivamente di tipo strutturale. 

La disciplina che fornisce questo tipo di risposte è la 

meccanica applicata, che studia la cinematica e la dinamica 

di sistemi di corpi variamente interconnessi. Cinematica e 

Dinamica sono termini di uso comune, ma sono spesso utilizzati 

in modo poco corretto. Senza addentrarci in eccessivi 

formalismi, precisiamo che l’analisi cinematica determina il 

modo in cui si muovono i corpi di un sistema (posizioni, velocità, 

accelerazioni) in relazione agli azionamenti (motori, 

camme) e ai vincoli. Viceversa, l’analisi dinamica determina 

le forze e le coppie che sono causa e/o effetto del movimento. 

In alcuni problemi è sufficiente limitare lo studio alla parte 

cinematica (ad esempio per la verifica di sincronismi e/o 

di possibili interferenze), mentre nei casi più generali è necessario 

completare le indagini con la determinazione delle 

forze in gioco (ad es. per trasmetterle allo strutturista o per 

scegliere componenti da catalogo). 

Gli strumenti di simulazione adatti a condurre queste particolari 

analisi sono i cosiddetti software multibody. 

All’interno di un ambiente multibody l’utente assembla virtualmente 

il sistema meccanico e procede con l’analisi della 

risposta nel dominio del tempo e/o delle frequenze. I codici 

commerciali offrono la possibilità di interagire direttamente 

con le geometrie CAD, a vantaggio dei tempi di modellazione 

e della qualità di visualizzazione. Per applicazioni di nicchia 

e per scopi di ricerca si utilizzano, tuttavia, efficacemente 

anche approcci prettamente analitici, con i quali il 

modello viene definito attraverso scrittura diretta delle equazioni 

di moto. 

Indipendentemente dallo strumento utilizzato, il passaggio 

fondamentale per giungere a risultati corretti ed affidabili 

nella simulazione multibody è rappresentato dalla fase di 

“virtualizzazione” del modello fisico. Con “virtualizzazione” 

si intende l’approssimazione di un sistema meccanico reale 

(infinitamente complesso), con una collezione di oggetti numerici 

pensati per riprodurne moto e proprietà. 

La schematizzazione virtuale può avvenire in modo più o meno 

raffinato, con conseguenze dirette sull’efficacia della simulazione. 

È compito del modellista scegliere le dimensioni, 

il grado di complessità e i dettagli del modello che vuole 

creare, considerando simultaneamente gli obiettivi da raggiungere, 

onere computazionale e il tempo a disposizione. Il 

miglior modello non è quello più dettagliato, ma quello che 

risponde in modo più veloce ed esauriente alle esigenze. 

Questa regola, che vale in generale per tutte le dimensioni 

del CAE, assume un ruolo decisivo nella simulazione 

multibody. Per queste ragioni è indispensabile provvedere ad 

una formazione teorico-pratica specifica per l’analista meccanico. 

EnginSoft propone un corso di modellistica multibody della 

durata di 2 giorni (2-3 Febbraio 2010, sede di Padova), rivolto 

a tutti i progettisti che affrontano problemi di cinematica 

e dinamica. Il corso è pensato e strutturato in modo da 

trasmettere in tempi brevi le nozioni per poter poi in seguito 

intraprendere le scelte per la modellazione multibody. Il 

corso sarà tenuto dal prof. Roberto Lot dell’Università di 

Padova in collaborazione con l’ing. Fabiano Maggio di 

EnginSoft. 

Per informazioni sui contenuti consultare il sito del consorzio 

TCN www.consorziotcn.it 

Per iscrizioni e informazioni generali consultare la sig.ra 

Mirella Prestini della segreteria del consorzio. 

E-mail: info@consorziotcn.it - Tel: +39 035 368711


Interview with Mr Sakae Morita, General 

Manager, Marketing and Mr Kentaro 

Fukuta of ELYSIUM Co., Ltd. Japan 

What are your impressions of the EnginSoft International 

Conference 2009? 

Mr. Morita: Indeed, we brought back many new discoveries 

from our first participation in the EnginSoft Conference. It 

has been particularly interesting to see that there are many 

joint efforts and activities between industry and the 

academia and that lots of successes are actually linked to 

these collaborations. Although we can see similar efforts in 

Japan, there is still a huge gap between industry and the 

universities. The attitude and openness in Italy and in the 

surrounding countries in Europe seem to be very good. 

Mr.Fukuta: Our time at the Conference in Bergamo was an 

extremely significant experience, more than I had anticipated 

before our trip to Europe. Much to our surprise, we met many 

participants from all over the world. This is one of the 

differences compared to CAE conferences in Japan. It’s great 

to have the opportunity to actively exchange technical 

information between organizations from different countries. 

Mr.Morita: From Professor Stefano Odorizzi’s keynote speech 

and my personal conversation with him, I was particularly 

impressed by the fact that EnginSoft is expanding its 

business not only in Italy but also internationally and this in 

harsh economic times. At Elysium, we are keen to establish a 

longterm relationship with EnginSoft. 

Bergamo’s medieval Città Alta - Impressions photographed by Mr. Morita / Mr Fukuta in 

October 2009 

Elysium Booth in the exhibition area 

Apart from the Conference, did you have time to explore 

Italy a bit? 

Mr.Morita: Luckily, we had enough time to stroll around in the 

old city of Bergamo and in Milan after the conference. What 

really moved me is the deep history and the beautiful 

harmony between the past and modernity. These are 

memories I brought back from looking at the old 

architectures and from our visits to some museums. 

Mr.Fukuta: I will remember my first visit to Italy for a 

long time to come. I was fascinated by the beautiful 

landscape of Bergamo and the wonderful dinner we 

have enjoyed in a restaurant on one of the surrounding 

hills of the city. 

Would you say that your presence, also as an 

exhibitor, at the Conference was effective? 

Mr.Morita: Certainly yes. There were quite a few 

positive discussions about our products and we could 

generate some leads for our future business in Europe. 

I am really grateful to Ms. Barbara Leichtenstern for 

her deep considerations and to everybody at EnginSoft. 

Mr.Fukuta: Thank you for all your support and for giving 

us such a good opportunity to meet the people of 

EnginSoft and the audience of the International 

Conference. 

This interview was conducted by Ms Akiko Kondoh 

Consultant for EnginSoft in Japan

New Year 

Greetings from 

Japan 

with best wishes from Akiko Kondoh 

It has been a great pleasure to launch 

the Japan Column in 2009. Sometimes 

inspiration is needed for product 

design and manufacturing. The same 

is true for CAE. I hope that the new 

encounter in the EnginSoft Newsletter, between Japanese and 

European (CAE) cultures, creates inspiration and motivation for 

2010. 

In the New Year, Shogatsu is generally celebrated on the first 

3 days of January. In Japan, this is the most important period 

to spend with family. Osechi-ryori are special side dishes which 

we enjoy on the first 3 days of the year. Osechi-ryori consist of 

traditional ingredients in Japanese cuisine, all of them have 

special meanings. For example, sea bream (tai) should bring 

luck (medetai), herring roe (kazunoko) sends out “a wish for 

prosperity to our descendants”, and sea tangle roll (kobumaki) 

means “happiness” (yorokobu). 

This Osechi-ryori 

are arranged on 

the Urushi, a 

Japanese lacquer 

tiered box. Urushi 

is the coating 

material made 

from refined and 

processed lacquer 

tree sap. Urushi 

has been used for 

the last thousands of years. Indeed, Japanese lacquering 

techniques had improved rapidly at a time more than 1500 

years ago. The black shining Urushi became a traditional craft 

and nowadays it is widely used for tableware, fine furniture and 

musical instruments. Urushi is resistant to humidity, heat, acid 

and alkali, but becomes depleted under extreme ultraviolet 

irradiation or desiccation. This is why Urushi was not much 

used for industrial products in the past. However, in recent 

years, Urushi has attracted people’s attention not only in Japan 

but around the world because of its unique glazing style and 

excellent characteristics. Today, Urushi is applied to brand new 

areas of MONODUKURI*, for example for the interior of cars and 

airplanes and the exterior of various electrical products by 

combining Urushi material characteristics and specific 

lacquering techniques. 

*MONODUKURI: Japanese for manufacturing and Japan’s spirit 

for excellence in manufacturing 


modeFRONTIER at the 2009 

MADYMO Users Meeting in 

Melbourne 

EnginSoft's partner in 

Australia, ADVEA 

Engineering, hosted their 

semi-annual event “The 

2009 MADYMO Users 

Meeting” in Melbourne, 

Australia on the 23rd & 

24th of November. 

The event attracted a widerange 

of engineers from the Asia Pacific region with a focus on 

automotive active/passive safety, biomechanics, pedestrian 

safety and DOE/optimization. 

Maciej Mazur, a University Student at the School of Aerospace, 

Mechanical and Manufacturing Engineering at RMIT, The Royal 

Melbourne Institute of Technology, one of Australia’s original 

and leading educational institutions, presented a DOE and an 

optimization study of a cast-aluminium servo motor housing. 

In his presentation, Maciej detailed how he coupled 

successfully modeFRONTIER with Catia and Abaqus for Catia to 

optimize the housing for weight and stiffness. 

For more information about this presentation and about 

modeFRONTIER and CAE in Australia, feel free to contact Mr. 

Ryan Adams, email: radams@advea.com, Manager ADVEA 

Engineering. www.advea.com 

Optimization Training Star- 

CCM+ AND modeFRONTIER 

in Göteborg, February 23 

In cooperation with CDadapco 

and FS Dynamics, 

EnginSoft Nordic will 

present a one-day hands-on 

training on optimization 

with modeFRONTIER and 

Star-CCM+. This training, to 

be held in Göteborg on 

February 23rd 2010, will 

teach how to automate and perform scripting of Star-CCM+ 

analyses, and how to setup an optimization together with 

modeFRONTIER. After an introduction and demonstration, 

training participants will be given a complete workshop to 

work through on their own. As such, they are expected to bring 

a laptop with Star-CCM+ for the exercises. After the workshop, 

the training will conclude with a discussion and a Q&A session. 

For more information, please contact Adam Thorp 

at info@enginsoft.se or visit http://nordic.enginsoft.com


Il mondo della forgiatura a stampi aperti, 

della laminazione piana e circolare, si è dato 

appuntamento a Padova per fare il punto 

sulle tecniche più avanzate di ottimizzazione 

di processo/prodotto. 

Dopo il successo dei primi due appuntamenti di Lecco, il 

3 aprile, dedicato allo stampaggio a caldo di acciaio, e di 

Bergamo, il 7 maggio, dedicato allo stampaggio a caldo di 

non ferrosi (ottone ed alluminio), EnginSoft ha voluto dedicare 

un pomeriggio al mondo della forgiatura a stampi 

aperti, della laminazione di prodotti lunghi e della laminazione 

circolare. 

L’invito è stato accolto da quasi una cinquantina di rappresentanti 

delle aziende più importanti in Italia che si 

occupano di trasformazione di acciaio a stampi aperti o 

per laminazione, desiderosi di conoscere le più avanzate 

Grande successo per il pomeriggio 

tecnologico - Forgiatura, Laminazione a 

Caldo di Prodotti Lunghi e Laminazione 

Circolare: Simulazione dei Processi: Nuovi 

Sviluppi, Vantaggi e Prospettive – 

organizzato il 24 giugno a Padova da 

EnginSoft, con la presenza di AFV Beltrame, 

Hydromec, FICEP e DIMEG – Università di 

Padova. 

tecniche di ottimizzazione di processo 

e prodotto. 

A fare gli onori di casa è stato Piero 

Parona, Sales Manager di EnginSoft, 

con una descrizione delle molteplici 

attività di EnginSoft nel campo della 

prototipazione virtuale e della 

strategicità dell’uso di queste tecniche 

nell’ottica di riduzione dei costi. 

Si è entrati quindi nel vivo dell’argomento 

con gli interventi dell’ing. Marcello Gabrielli, 

sempre di EnginSoft, che hanno riguardato le tecniche di 

simulazione numerica dei processi di stampaggio a stampi 

aperti e laminazione con il software Forge di Transvalor. A 

partire da una analisi del modo attuale di progettare le sequenze 

di stampaggio, si è costruito un percorso innovativo 

dove, grazie alla simulazione applicata ad esempi reali 

su particolari noti ai presenti, si sono evidenziati tutti i 

vantaggi concreti ottenibili. Le recenti modifiche apportate 

al software grazie ad EnginSoft ed agli utilizzatori italiani, 

consentono ora di simulare per lo stampaggio a 

stampi aperti cicli anche molto complessi, con rotazioni 

relative di pezzo e\o 

mazze. Sono stati mostrati 

esempi concreti di 

forgiatura, ricalcatura in 

chiodaia e con mazze, 

blumatura, compattazione, 

sbozzatura, segnatura, 

bigornatura, evidenziando 

per ciascuno di essi i risultati più significativi forniti 

dall’approccio virtuale. Per un caso di riscaldo di un 

lingotto poligonale è stato mostrato un approccio di ottimizzazione, 

ottenuto mediante l’integrazione con 

modeFRONTIER, che ha consentito un risparmio di 4 ore di 

permanenza in forno, garantendo comunque il riscaldo a 

cuore del lingotto. Altrettanto significativi i risultati ottenuti 

grazie alla simulazione del processo di tempra, in termini 

di previsione delle fasi, durezza e distorsioni. 

L’ing. Carlo Contri di Hydromec (www.hydromec.it) ha 

quindi mostrato le novità dei propri impianti per la forgiatura 

e la laminazione circolare, sottolineando come grazie 

a Forge, utilizzato attraverso la collaborazione con 

EnginSoft, per alcuni propri clienti, 

è stato fornito un servizio di codesign 

che ha consentito sia di valutare 

a priori se una macchina è in 

grado di produrre un certo particolare, 

sia di ridurre significativamente 

i sovrametalli, fornendo 

quindi un servizio “chiavi in mano” 

ai propri clienti. 

L’ing. Stefano Fongaro di FICEP 

(www.ficep.it) ha quindi mostrato come ha affrontato e risolto 

il problema del taglio delle barre mediante nuove segatrici 

ad alta velocità per barre fino a 800mm di diametro. 

Ritornando a tematiche relative alla simulazione di processo, 

la parte relativa alla simulazione della laminazione 

di prodotti lunghi è stata affidata alla testimonianza di un 

utilizzatore, la AFV Beltrame SpA (www.beltrame.it) di 

Vicenza. A partire dai risultati della simulazione della singola 

gabbia di laminazione, utili per una prima valutazione 

della deformazione del materiale, si è passati all’anali-

si di un treno completo di quattro gabbie 

di laminazione per l’ottenimento di 

un profilo IPE. I risultati ottenuti per 

questo particolare e per altri profili mostrati 

(bulbo, cingolo, T80) hanno dimostrato 

come la simulazione ha effettivamente 

consentito di valutare a priori le 

corrette calibrature delle gabbie. 

Si è passati infine all’analisi del processi 

di laminazione circolare, per il quale 

sono state analizzate le fasi di forgiatura 

e tranciatura dell’anello, risolte in 

tempi molto rapidi grazie all’approccio 2D, e quindi la simulazione 

della laminazione vera e propria. Particolarità 

di questo processo è la moltitudine di cinematiche adottate 

per laminare (a rack assiale fermo o mobile, con coni 

fermi o mobili, con rullo ad asse verticale o inclinato, fermo 

o mobile) ed inoltre le cinematiche stesse sono funzione 

della crescita dell’anello. Nella pratica si definiscono 

delle curve di laminazione nel software del laminatoio ed 

i tool si muovono di conseguenza. Grazie alla flessibilità 

di Forge nella definizione delle cinematiche, si è mostrato 

come forge riesca a replicare in modo molto accurato 

quanto avviene nella realtà, aspetto questo dimostrato 

con esempi partici di laminazione di anelli di geometria 

dalla più semplice, rettangolare, alle più complesse, per 

anelli profilati, sagomati, flange e rulli. Non meno interessante 

è stato l’intervento del dott. Andrea Ghiotti del DI- 

MEG – Università di Padova (www.dimeg.unipd.it) che ha 

mostrato le attività del DIMEG nella simulazione, tramite 

Forge ed un approccio a reti neurali, della distorsione degli 

anelli nelle fasi di raffreddamento. 

A completare la sezione tecnica, una dimostrazione dal vivo 

di utilizzo di Forge, curata dall’ing. Andrea Pallara di 

EnginSoft, che ha impostato il caso di una sequenza di fucinatura 

e stampo aperto ed una laminazione di un anello 

a sezione rettangolare. La demo live ha evidenziato come 

questi strumenti siano ormai molto facili da utilizzare, sia 

nella fase di preparazione delle simulazioni, che nella fase 

di interpretazione dei risultati. 

Un appuntamento importante, dicevamo, dove alle presentazioni 

previste in agenda è seguito un partecipato dibattito 

tra i relatori ed il pubblico, dal quale è emerso come 

lo strumento sia già molto maturo per quanto riguarda le 

tematiche di forgiatura. Per quanto riguarda la laminazione 

circolare, l’aspetto critico sembra essere la precisione 


con la quale si riescono a modellare le curve di laminazione 

reali, in modo da prevedere comportamenti anomali del 

materiale durante il processo. Forge è stato migliorato in 

modo importante per questi aspetti specifici, grazie alla 

collaborazione con dei produttori di laminatoi. 

Volendo sintetizzare, quanto mostrato nel workshop ha dimostrato 

come questi strumenti siano realmente in grado 

di dare una maggior coscienza del proprio modo di produrre 

e come le esperienze fatte con Forge siano utili sia a far 

crescere molto rapidamente chi si avvicina a questo mondo, 

e a far diventare “patrimonio aziendale” le procedure 

di stampaggio ottimizzate in tal modo. Ultimo aspetto 

non meno importante è il fatto che, grazie ai concreti vantaggi 

ottenibili, è possibile ammortizzare l’investimento 

in tempi molto rapidi. 

In questo momento di difficoltà legata alla congiuntura 

economica, è necessario cogliere l’occasione per investire 

in metodologie innovative, in grado di dare maggiore 

competenza e conoscenza del proprio processo ai reparti 

di progettazione e produzione, di ridurre i costi, e di promuovere 

la propria immagine aziendale aumentando le 

possibilità di co-design nei confronti dei propri clienti. 


Ing. Marcello Gabrielli - EnginSoft 


EnginSoft sponsorizza lo 

sport in trentino 

Squadra di calcio femminile di serie A2 ACF TRENTO. 

www.calciotrento.it


Il mondo dello stampaggio a freddo di viterie 

e minuterie metalliche, si è dato 

appuntamento a Bergamo per fare il punto 

sulle tecniche più avanzate di ottimizzazione 

di processo/prodotto. 

Il 25/10 a Bergamo si è tenuto l’ultimo appuntamento del 

2009 sulla simulazione dei processi di stampaggio dei metalli, 

dedicato questa volta al mondo dello stampaggio a freddo. 

L’invito è stato accolto da una quarantina di rappresentanti 

delle aziende più importanti in Italia che si occupano di 

stampaggio a freddo di acciaio nei campi della viteria e bulloneria, 

della minuteria metallica e di altri particolati ottenuti 

per stampaggio, desiderosi di fare il punto sui vantaggi 

offerti dagli strumenti di simulazione nel miglioramento dei 

processi/prodotti. 

Grande successo per il pomeriggio 

tecnologico - Stampaggio a Freddo di Viterie 

e Minuterie Metalliche: Simulazione dei 

Processi: Nuovi Sviluppi, Vantaggi e 

Prospettive – organizzato il 25 ottobre a 

Bergamo da EnginSoft, con la presenza di 

SACMA Limbiate, Panzeri, Omega Ifs. 

A rompere il ghiaccio, come di consueto, è stato Piero 

Parona, Sales Manager di EnginSoft, con una descrizione delle 

molteplici attività di EnginSoft nel campo della prototipazione 

virtuale e della strategicità dell’uso di queste tecniche 

nell’ottica di riduzione dei costi. 

L’intervento successivo, a cura dell’ing. Marcello Gabrielli, 

sempre di EnginSoft, ha riguardato le tecniche di simulazione 

numerica dei processi di stampaggio a freddo con il software 

ColdForm di Transvalor. Attraverso esempi reali si è analizzato 

come il software sia un valido supporto alle decisioni 

che i tecnici devono 

prendere nella messa a 

punto del processo produttivo, 

per ogni operazione 

di stampaggio. Si è 

partiti dal processo di 

stampaggio da filo, trattando 

alcune sequenze di 

formatura per dei particolari di minuteria e viteria e mostrando 

come l’analisi dei contatti, del flusso di materiale e delle 

ripieghe possa aiutare ad individuare i problemi e mostrare la 

via per risolverli. Si è passati quindi all’analisi delle sollecitazioni 

sugli stampi, mostrando come intervenire per ridurne 

l’usura e migliorarne la vita utile, una volta individuate le zone 

di massima sollecitazione. Per alcune configurazioni si è 

affrontata una messa a punto delle condizioni di interferenza 

(blindaggio) per garantire il corretto precarico alle matrici. 

Si è quindi accennato ai risultati ottenibili nell’analisi 

della tranciatura e della rollatura dei filetti. 

Sono stati quindi analizzati dei casi di stampaggio e tranciatura 

di lamiere, con cenni relativi all’influenza dell’anisotropia 

sul risultato dell’imbutitura ed è stato mostrato un approccio 

differente, con i software della FTI 

(www.forming.com) per i casi di stampaggio di lamiera sottile, 

per la quale vengono calcolati gli spessori, le zone di cedimento 

e di grinzatura. 

In conclusione, sono stati mostrati esempi di messa in opera 

di rivetti e viti, dove il software ha consentito di valutare 

le resistenza degli accoppiamenti all’applicazione di condizioni 

di sollecitazione assiali e tangenziali. 

Ha quindi preso la parola L’ing. Brigatti della 

SACMA Limbiate (http://www.sacmalimbiate.it), 

riferimento per le macchine automatiche per lo 

stampaggio a freddo, che ha mostrato quali sono 

le tecnologie che vengono adottate per migliorare 

precisione e prestazioni, quali ad esempio 

la slitta a guida conica, il sistema di cambio 

rapido degli stampi ed i sistemi di microregolazione 

degli aggiustamenti. Si è quindi soffermato 

sugli ultimi sviluppi nel campo dello stampaggio 

a tiepido, evidenziando i vantaggi di 

questa tecnologia.

Lo spazio dedicato alle testimonianze degli utilizzatori si è 

aperto con la presentazione dell’ing. Giussani della Panzeri 

(http://www.panzerionline.com), che ha mostrato come 

Coldform può essere utilizzato per la verifica e la messa a 

punto del processo di tranciatura di rondelle. Interessante lo 

studio effettuato con la collaborazione di EnginSoft e 

dell’Università di Trento, mediante il quale Panzeri è ora in 

grado di caratterizzare i materiali per la simulazione numerica, 

ma anche di certificarne la qualità per la produzione. 

Si è passati infine alla presentazione dell’ing. Wegner di OME- 

GA IfS (http://www.omegaifs.it), che ha mostrato l’utilizzo 

del software in tre casi particolari: un perno, dove la formatura 

del profilo superiore dell’ingranaggio portatava alla formazione 

di bave, una doppia estrusione, dove è stato usato 

Coldform per valutare la forma della superficie libera ed una 

forcella, per la quale le analisi hanno consentito di rimediare 

ad una rottura delle matrici. 

A completare la sezione tecnica, una dimostrazione dal vivo 

di utilizzo di ColdForm, curata dall’ing. Andrea Pallara di 

EnginSoft, che ha impostato il caso di una sequenza di formatura 

di una vita a partire dallo spezzone di filo, passando 

per l’estrusione del gambo, la formatura della testa e la creazione 

dell’impronta. La demo live ha evidenziato come questi 

strumenti siano ormai molto facili da utilizzare, sia nella fase 

di preparazione delle simulazioni, che nella fase di interpretazione 

dei risultati. 

Al termine delle presentazioni i presenti hanno avuto lo spazio 

per porre delle domande ed ottenere degli approfondi- 


menti da tutti i relatori presenti. Volendo sintetizzare, quanto 

mostrato nel workshop ha dimostrato come questi strumenti 

siano realmente in grado di dare una maggior coscienza 

del proprio modo di produrre e come le esperienze fatte 

con ColdForm siano utili sia a far crescere molto rapidamente 

chi si avvicina a questo mondo, e a far diventare “patrimonio 

aziendale” le procedure di stampaggio ottimizzate in 

tal modo. Ultimo aspetto non meno importante è il fatto che, 

grazie ai concreti vantaggi ottenibili, è possibile ammortizzare 

l’investimento in tempi molto rapidi. 

In questo momento di difficoltà legata alla congiuntura economica, 

è necessario cogliere l’occasione per investire in metodologie 

innovative, in grado di dare maggiore competenza 

e conoscenza del proprio processo ai reparti di progettazione 

e produzione, di ridurre i costi, e di promuovere la propria immagine 

aziendale aumentando le possibilità di co-design nei 

confronti dei propri clienti. 


Ing. Marcello Gabrielli - EnginSoft 


Bilancio del Ciclo di Workshop 

dedicati alla Simulazione dei 

Processi di Deformazione dei 

Metalli 

Con l’appuntamento del 25/10 a Bergamo si è quindi 

chiuso il ciclo di workshop dedicati alla simulazione dei 

processi di deformazione dei metalli: il 03/04 a Lecco 

per lo stampaggio a caldo di acciaio, il 07/05 a Bergamo 

per lo stampaggio dei non ferrosi, il 24/06 a Padova per 

la forgiatura, la laminazione a caldo di prodotti lunghi e 

la laminazione circolare.Volendo fare un bilancio dell’intero 

ciclo, la partecipazione di oltre 180 persone di quasi 

90 aziende ha decretato il pieno successo di questa 

iniziativa e con diverse delle aziende presenti si sta iniziando 

un percorso per l’introduzione di questi strumenti 

nella pratica progettuale quotidiana. Probabilmente le 

difficoltà economiche legate alla crisi hanno impedito ad 

altri di partecipare. 

Questo ci ha spinto, per 

l’anno 2010, ad organizzare 

degli eventi simili, 

però avvalendoci di webinar che sfruttano la rete internet 

per proporre gli stessi contenuti, senza obbligare le persone 

ad effettuare delle trasferte. Rimanete sintonizzati 

sul nostro sito www.enginsoft.it per le date di questi 

eventi o contattateci per degli incontri specifici presso la 

vostra sede. 

Le date sono: 

11 Febbraio - 12 Marzo - 15 Aprile - 13 Maggio 

www.enginsoft.it/webinar


EnginSoft Event Calendar 

ITALY 

14-17 April - METEF 2010 - International aluminium and 

foundry exhibition. Visit the EnginSoft Booth where we 

present news on process simulation technologies related 

to MAGMA, Forge, Coldform, AdvantEdge… 

www.metef.com 

14-15 April 2010 - Affidabilità e Tecnologie 2010 

Meet EnginSoft in the exhibition and learn from our 

Seminar on Innovation in industry through Virtual 

Prototyping! www.affidabilita.eu 

27-28 May - International modeFRONTIER Users’ Meeting 

2010. Starhotel Savoia Excelsior Palace, Trieste 

Learn how modeFRONTIER, the leading multidisciplinary & 

multi-objective design optimization tool, is used globally 

in many industries to better understand product 

development processes, and achieve higher quality at 

reduced cost, allowing them to meet the challenge of 

producing better products faster! 

www.esteco.com 

Fall 2010 – EnginSoft International CAE Conference 2010 

Exact dates and venue will be announced soon! 

www.caeconference.com 

FRANCE 

17-18 Mars 2010 - Micado : Etats Généraux Micado : "La 

contribution de l’ingénierie numérique à l’ECO conception" 

Evry (91). Edition exceptionnelle en partenariat avec la 

Chambre de Commerce de l'Essonne sur le thème: "La 

contribution de l'Ingénierie Numérique à l'ECO 

Conception". www.af-micado.com 

EnginSoft France 2010 Journées porte ouverte. Dans nos 

locaux à Paris et dans d’autres villes de France et de 

Belgique, en collaboration avec nos partenaires. Prochaine 

événement: Journées de présentation modeFRONTIER 

2010 Séminaires Simulation de Process et Optimisation 

EnginSoft France Boulogne Billancourt – Paris.S eminars 

hosted by EnginSoft France and EnginSoft Italy. Veuillez 

contacter Marjorie Sexto, info@enginsoft.com, pour plus 

d'information ou visitez : www.enginsoft-fr.com 

21-23 June – ASMDO 2010 3rd International Conference 

on Multidisciplinary Design Optimization and Applications 

- Co-sponsored by ISSMO, ESTP, EnginSoft, and NAFEMS 

Paris. ASMDO 2010 will bring together scientists and 

practitioners working in different areas of engineering 

optimization! www.asmdo.com 

GERMANY 

Please stay tuned to www.enginsoft-de.com and contact 

Stephanie Koch at: info@enginsoft.com for more 

information. 

Seminars Process Product Integration. EnginSoft GmbH, 

Frankfurt Office. How to innovate and improve your 

production processes! Seminars hosted by EnginSoft 

Germany and EnginSoft Italy. Dates will be announced in 

early 2010. 

modeFRONTIER Seminars 2010. EnginSoft GmbH, Frankfurt 

am Main: 26 January, 16 February, 9 March, 30 March, 20 

April, 18 May, 15 June 

UK 

Please stay tuned to www.enginsoft-uk.com and contact 

Bipin Patel at: info@enginsoft.com for more information. 

modeFRONTIER Workshops at Warwick Digital Lab 

Please check www.enginsoft-uk.com for next dates! 

25th February - Technical Seminar on Manufacturing 

Process Simulation Cranfield University 

Attend EnginSoft UK’s FREE Seminar and learn how stateof-the-art 

simulation tools can help reduce development 

time and drastically cut costs in manufacturing processes. 

The seminar will provide an interesting and effective 

overview of the most modern CAE technologies available 

today and how they can enhance your design production 

processes when combined with world-class expertise. 

To register online, please visit: www.enginsoft-uk.com 

SPAIN 

24 - 27 February - 9th International Symposium on 

Computer Methods in Biomechanics and Biomedical 

Engineering. Valencia. For more information and to 

arrange a meeting with Gino Duffett, APERIO Tecnología, 

please contact: g.duffett@aperiotec.es, www.aperiotec.es 

SWEDEN 

23 February - Optimization Training Star-CCM+ and 

modeFRONTIER. Goeteborg. In cooperation with CDadapco 

and FS Dynamics, Esteco EnginSoft Nordic will 

present a one-day hands-on training on optimization with 

modeFRONTIER and Star-CCM+ 

modeFRONTIER Courses scheduled so far for 2010: 

Esteco EnginSoft Nordic Office, Lund

21-22 January - Introduction to modeFRONTIER 

9-10 February - Introduction to modeFRONTIER 

11 February - Robust Design with modeFRONTIER 

For further information, please contact Adam Thorp at: 

info@esteconordic.se 

USA 

Courses on: Design Optimization with modeFRONTIER 

Ozen Engineering, Sunnyvale – Silicon Valley, CA 

Learn about Optimization coupled with ANSYS. OZEN can 

easily help you out automating the search for the optimal 

design. The primary audience for this course includes 

ANSYS Classic and Workbench users as well as new 

modeFRONTIER users who want to have a complete 

overview to all software capabilities. Stay tuned to our US 

partner’s website for the next events in the USA: 

www.ozeninc.com - info@ozeninc.com 

EUROPE, VARIOUS LOCATIONS 

modeFRONTIER Academic Training 

Please note: These Courses are for Academic users only. 

The Courses provide Academic Specialists with the fastest 

route to being fully proficient and productive in the use of 

modeFRONTIER for their research activities. The courses 

combine modeFRONTIER Fundamentals and Advanced 

Optimization Techniques. For more information, please 

contact Rita Podzuna, info@enginsoft.it 

To meet with EnginSoft at any of the above events, please 

contact us: info@enginsoft.com 

Optimization Crossword Puzzle 


Search the words linked to modeFRONTIER® in the puzzle on the left, then put together the remaining letters starting from 

top: You will discover a nice message from EnginSoft! 

O B R E T U P M O C E S 

B P S T A T I S T I C S 

J T T W I M C D M T S I 

E S C I N H C E T A H M 

C I L E M T O O L M O U 

T M U G N I L E D O M L 

I P S S F R Z T R T E A 

V L T E M O G A O U D T 

E E E D M G E R T A N O 

G X R O I L N E S I O R 

F T T N I A R T S N O C 

K R I G I N G I S E D N 

SOLUTION 

_ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ 

EnginSoft CAE Webinars in 2010 

EnginSoft's engineering team will conduct a new series of 

CAE webinars in 2010. A variety of CAE topics will be 

covered by our experts based on EnginSoft 

multidisciplinary expertise and tradition. 

The CAE webinars will demonstrate the best ways to 

innovate industrial processes using Virtual Prototyping. 

Stay tuned on webinars calendar: 

www.enginsoft.it/webinar 

ALGORITHM 

AUTOMATIC 

CLUSTER 

COMPUTER 

CONSTRAINT 

DEMO 

DESIGN 

ITERATE 

KRIGING 

MCDM 

MODELING 

MOGA 

NODE 

OBJECTIVE 

OPTIMIZATION 

SIMPLEX 

SIMULATOR 

STATISTICS 

TECHNICS 

TOOL

NUOVO LIBRETTO - NEW PUBBLICATION 

CORSI DI ADDESTRAMENTO SOFTWARE 2010 

SOFTWARE TRAINING COURSES 2010 

EnginSoft è la società italiana di maggior consistenza e tradizione nel 

settore del CAE ove, grazie alla multidisciplinarietà delle competenze, 

è in grado di proporsi come partner unico per le aziende. 

L'attività di formazione rappresenta da sempre uno dei tre maggiori 

obiettivi di EnginSoft accanto alla distribuzione ed assistenza del 

software ed ai servizi di consulenza e progettazione. 

Per ciascuno dei possibili livelli cui la richiesta di formazione può porsi 

(quella del progettista, dello specialista o del responsabile di 

progettazione), EnginSoft mette a disposizione la propria esperienza 

per accelerare i tempi del completo apprendimento degli strumenti 

necessari con una gamma completa di corsi differenziati sia per livello 

(di base o specialistico), che per profilo professionale dei destinatari 

(progettisti, neofiti od analisti esperti). 

La finalità è sempre di tipo pratico: condurre rapidamente all'utilizzo 

corretto del codice, sviluppando nell'utente la capacità di gestire 

analisi complesse attraverso l'uso consapevole del codice di calcolo. 

Per questo motivo ogni corso è diviso in sessioni dedicate alla 

presentazione degli argomenti teorici alternate a sessioni 'hands on', 

in cui i partecipanti sono invitati ad utilizzare attivamente il codice di 

calcolo eseguendo applicazioni guidate od abbozzando, con i 

suggerimenti del trainer, soluzioni per i problemi di proprio interesse, e 

discutendone impostazioni e risultati. 

Anche nel 2010 EnginSoft propone una serie completa di corsi che 

coprono le necessità di formazione all'uso dei diversi software 

commercializzati. 

Le novità proposte sono diverse, a conferma che l'idea che EnginSoft 

ha della formazione non è una realtà statica che si ripropone uguale a 

se stessa di anno in anno, ma è un divenire, guidato dall'esperienza 

accumulata negli anni, dall'evoluzione del software e dalle esigenze 

delle società che si affidano a noi per la formazione del proprio 

personale. 

L'offerta dei corsi ANSYS è stata ridefinita per adeguarsi sia 

all'evoluzione del software ed alle caratteristiche della recentissima 

versione 12.1 che all' introduzione di nuovi moduli e solutori 

recentemente resi disponibili. 

In tale senso si segnalano: 

• il corso dedicato allo studio con ANSYS delle strutture in materiale 

composito, in particolare attraverso l'utilizzo del modulo dedicato 

ACP; 

• il corso per il solutore esplicito ANSYS WORKBENCH 

EPLICIT/STR integrato nell' ambiente WorkBench e quello per il 

solutore esplicito generalizzatoAUTODYN; 

• i nuovi corsi, relativi alle applicazioni specializzate per la 

progettazione offshore, AQWA (codice per lo studio dell' 

idrodinamica di strutture galleggianti) ed ASAS (codice 

specializzato per la verifica di strutture OffShore); 

• in campo elettromagnetico viene introdotto un corso per ANSOFT- 

MAXWELL, software che rappresenta il riferimento nel settore delle 

Training Center EnginSoft 

• un modulo relativo al solutore ANSYS POLYFLOW dedicato allo 

studio di processi quali l' estrusione, la termoformatura, il soffiaggio 

di polimeri o del vetro; 

• in campo fluidodinamico è da rimarcare l' introduzione, accanto ai 

corsi classici tradizionalmente erogati, di corsi specifici per il 

solutoreANSYS-FLUENT. 

Sono stati inoltre rivisti ed aggiornati i corsi relativi a tutti gli altri 

software sostenuti da EnginSoft per adeguarli allo stato attuale delle 

relative distribuzioni. 

Dal punto di vista organizzativo nel 2010 tutte le cinque sedi EnginSoft 

saranno impegnate nella formazione, dando la possibilità agli utenti di 

scegliere la location a loro più conveniente in termini di vicinanza 

geografica alla propria società. 

Tutto questo a riprova dell'impegno nella formazione che, per 

EnginSoft, è e rimane un punto fondamentale della politica aziendale, 

un impegno costante verso l'eccellenza, un servizio per fare crescere i 

nostri clienti e, se lo desiderano, crescere con loro. 

analisi elettromagnetiche in bassa frequenza; www.enginsoft.it/corsi 

Key partner in Design Process Innovation

software training courses 2010 corsi di addestramento ... - EnginSoft

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?