About this Abstract |
Meeting |
TMS Specialty Congress 2025
|
Symposium
|
8th World Congress on Integrated Computational Materials Engineering (ICME 2025)
|
Presentation Title |
Linking Multiple Length Scales Using Material Data Driven Design (MAD3) |
Author(s) |
David Montes De Oca Zapiain, Hojun Lim |
On-Site Speaker (Planned) |
David Montes De Oca Zapiain |
Abstract Scope |
Metal alloys used in stamping and forming processes exhibit polycrystalline structures at the lower length scale that cause the metal to display plastic anisotropy. Accurate predictions of the metal’s plastic anisotropy are crucial in manufacturing given the effect it has on the macro scale. Material Data Driven Design (MAD3) is an innovative software that leverages the power of machine learning to link the micro and macro scales and thus modernize the forming and stamping processes of sheet metals by predicting the parameters that characterize the load-dependent behavior of a metal alloy 1000 times faster than existing solutions. This software is conveniently packaged in a simple and easy-to-use graphical user interface that is deployed using cloud computing. In this talk, we present the structure and functionality of MAD3 and how this technology can be obtained by external users.
SNL is managed and operated by NTESS under DOE NNSA contract DE-NA0003525. SAND2024-14838A |
Proceedings Inclusion? |
Undecided |