Deep Learning Sequence Methods in Multiphysics Modeling of Steel Solidification

Metals

DiabW. Abueidda¹  Seid Koric¹ 

[1] National Center for Supercomputing Applications, University of Illinois at Urbana Champaign, Urbana, IL 61801, USA;

关键词: sequence deep learning;    neural networks;    casting;    steel;    solidification;    multiphysics;   

DOI  :  10.3390/met11030494

来源: DOAJ

【 摘 要 】

The solidifying steel follows highly nonlinear thermo-mechanical behavior depending on the loading history, temperature, and metallurgical phase fraction calculations (liquid, ferrite, and austenite). Numerical modeling with a computationally challenging multiphysics approach is used on high-performance computing to generate sufficient training and testing data for subsequent deep learning. We have demonstrated how the innovative sequence deep learning methods can learn from multiphysics modeling data of a solidifying slice traveling in a continuous caster and correctly and instantly capture the complex history and temperature-dependent phenomenon in test data samples never seen by the deep learning networks.

【 授权许可】

Unknown