[Dottorcomp] Avviso di seminario - martedi` 22 giugno (ore 16), Dipartimento di Ingegneria Civile e Architettura

Mar 15 Giu 2021 11:46:16 CEST

Car* collegh*,

ricevo e inoltro l'avviso di seminario qui sotto.

Buona giornata,
Lorenzo Tamellini

---------- Forwarded message ---------
Da: Sonia Padovan <sonia.padovan a unipv.it>
Date: lun 14 giu 2021 alle ore 17:16
Subject: Invited Lectures on M&S for AM: Prof. Gregory Wagner (22/06/2021)
To: Sonia Padovan <sonia.padovan a unipv.it>

Carissimi,

condivido con voi l'annuncio del terzo evento della serie di *"Invited
Lectures on Modeling and Simulation for Additive Manufacturing"*
organizzate dal gruppo di Meccanica Computazionale e Materiali Avanzati
<https://compmech.unipv.it/>. Il terzo seminario avrà come invited speaker
il *Prof. Greg Wagner* (Northwestern University - Evanston (IL), US) e si
terrà *martedì 22 giugno* alle *ore 16.00* in modalità online:

*Zoom Meeting link:*
https://us02web.zoom.us/j/86558894136?pwd=K0ZpTlNJYnJ1dFFTelFCQjNZeVhGZz09
*Meeting ID:* 865 5889 4136
*Passcode:* compmech21

*YouTube Live Channel:*
https://www.youtube.com/channel/UCbuPu16F7DAuqSEX63agXyA

Sentitevi liberi di estendere l’invito a chiunque pensiate possa essere
interessato.

Cordiali saluti,
Sonia Padovan

*--*
*Sonia Padovan*
CompMech Group
Dept. Civil Engineering and Architecture
University of Pavia
Via Ferrata, 3 - 27100 Pavia, Italy
Phone  +39 0382 985463
E-mail  sonia.padovan a unipv.it
https://compmech.unipv.it/

*Invited Lectures on Modeling and Simulation for Additive Manufacturing :*
*Multiphysics and Data-Driven Process-Structure Simulations for Additive
Manufacturing in Metals*
*Invited Speaker: Prof. Greg Wagner*
*Tuesday, June 22 @ 4:00 p.m. (CEST)* *Zoom Meeting*
https://us02web.zoom.us/j/86558894136?pwd=K0ZpTlNJYnJ1dFFTelFCQjNZeVhGZz09
<https://unipv.us1.list-manage.com/track/click?u=a3702953383182b6dd4674871&id=9370d06662&e=aa961e0452>

*Meeting ID: 865 5889 4136 Passcode: compmech21*
*Live broadcasted also on our YouTube channel [LINK
<https://unipv.us1.list-manage.com/track/click?u=a3702953383182b6dd4674871&id=d95150bddf&e=aa961e0452>]
*

Additive manufacturing (AM) brings advantages for the fabrication of metal
parts compared with traditional techniques, including the ability to create
complex geometries with reduced production lead time and less material
waste. However, the intricacy of the additive process and the extreme
thermal environments involved can lead to material defects, heterogeneous
microstructures, and wide variation in properties, leading to unpredictable
and sometimes inferior performance of AM-built parts. Computational
simulation can be used to understand and predict the effects of various
process parameters on the resulting material, and to suggest strategies to
optimize ultimate part performance. In this talk, I present computational
approaches to this problem developed in my research group that are based on
a combination of detailed multi-physics modeling with data-driven
techniques using machine learning. Examples include the use of
melt-pool-scale thermal-fluid simulations to train a neural network for
fast temperature predictions near the laser track, and the use of thermal
history data in a convolutional neural network (CNN) to predict
microstructure and properties.
Greg Wagner received his Ph.D. in Mechanical Engineering from Northwestern
University in 2001. He spent over 12 years as a staff member and later
manager in the Thermal/Fluid Science and Engineering department at Sandia
National Laboratories in Livermore, CA, where his work included multiscale
and multi-physics computational methods, multiphase and particulate flow
simulation, extended timescale methods for atomistic simulation, and
large-scale engineering code development.  In January 2015 he joined the
faculty of the Mechanical Engineering department at Northwestern. His
current research focuses on applying novel simulation methods and high
performance computing to multiphase flows and flows with complex and moving
geometries. He is the author or co-author of over 60 journal articles, and
his awards include the Zienkiewicz Prize for Numerical Methods in
Engineering and the NIST Additive Manufacturing Benchmark Challenge award.
In 2021 he was named the Bette and Neison Harris Chair in Teaching
Excellence.
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