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.

翻译：利用计算实验探索优化材料结构以实现目标性能的工艺路径，需要理解工艺-微观结构-性能之间的关联机制。这要求采用多尺度方法连接宏观尺度（工艺参数）、介观尺度（均质化性能）与微观尺度（晶体学织构）。由于该问题的多尺度建模特性，随着决策树深度增加，可行工艺路径数量呈指数级增长，而传统模拟器的计算速度面临临界瓶颈。为降低给定载荷条件下微观组织演化的预测计算负荷，我们提出了一种融合物理约束的神经网络方法。该网络旨在学习各基础工艺步骤下微观组织的演化规律。实验表明，该方法在寻找最优工艺路径方面兼具高效性与鲁棒性。本研究将神经网络方法应用于铜微观结构的均质化刚度优化，在十步工艺实验中，该方法相比传统有限元模拟器实现了686倍的加速，且最终均质化刚度的计算误差仅为0.053%。