2023/04/11 by Junrong Lin, Lin, Junrong, Mahmudul Hasan +7 · 1 citation
Engineering · Materials Science · #Computational Engineering #FOS: Computer and information sciences #FOS: Physical sciences #Finance #Machine Learning (cs.LG) #Machine Learning in Materials Science #Materials Science (cond-mat.mtrl-sci) #Metal Forming Simulation Techniques #Metallurgy and Material Forming #and Science (cs.CE)
paper · pdf · doi:10.48550/arxiv.2305.00003
openalex publication_date 2023/04/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
Computational experiments are exploited in finding a well-designed processing path to optimize material structures for desired properties. This requires understanding the interplay between the processing-(micro)structure-property linkages using a multi-scale approach that connects the macro-scale (process parameters) to meso (homogenized properties) and micro (crystallographic texture) scales. Due to the nature of the problem's multi-scale modeling setup, possible processing path choices could grow exponentially as the decision tree becomes deeper, and the traditional simulators' speed reaches a critical computational threshold. To lessen the computational burden for predicting microstructural evolution under given loading conditions, we develop a neural network (NN)-based method with physics-infused constraints. The NN aims to learn the evolution of microstructures under each elementary process. Our method is effective and robust in finding optimal processing paths. In this study, our NN-based method is applied to maximize the homogenized stiffness of a Copper microstructure, and it is found to be 686 times faster while achieving 0.053% error in the resulting homogenized stiffness compared to the traditional finite element simulator on a 10-process experiment.