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.

探索计算实验在优化材料结构以获得期望性能方面的应用，并利用多尺度方法研究处理 - 微观结构 - 材料性能的相互作用，开发基于神经网络的方法来减轻预测微观结构演变的计算负担，同时实现更快速且更优的材料优化。