Lin, J., Hasan, M., Acar, P., Blanchet, J., & Tarokh, V. (2023). Neural network accelerated process design of polycrystalline microstructures. Materials Today Communications, 36, 106884. https://doi.org/10.1016/j.mtcomm.2023.106884
Abstract
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 …
Authors
Junrong Lin, Mahmudul Hasan, Pınar Acar, Jose Blanchet, Vahid Tarokh
Publication date
2023/8/1
Journal
Materials Today Communications
Volume
36
Pages
106884
Publisher
Elsevier
