In this study, we developed an inverse analysis framework that proposes a microstructure for dual-phase (DP) steel that exhibits high strength and ductility. The inverse analysis method proposed in this study involves repeated random searches on a model that combines a generative adversarial network (GAN), which generates microstructures, and a convolutional neural network (CNN), which predicts the maximum stress and working limit strain from DP steel microstructures. GAN was trained using images of DP steel microstructures generated by the phase-field method. CNN was trained using images of DP steel microstructures, the maximum stress and the working limit strain calculated by the dislocation-crystal plasticity finite element method. The constructed framework made an efficient search for microstructures possible because of a low-dimensional search space by a latent variable of GAN. The multiple deformation modes were considered in this framework, which allowed the required microstructures to be explored under complex deformation modes. A microstructure with a fine grain size was proposed by using the developed framework.

翻译：本研究开发了一种逆向分析框架，用于提出具有高强度和高塑性的双相（DP）钢微观结构。所提出的逆向分析方法对结合了生成微结构的生成对抗网络（GAN）与预测DP钢微结构最大应力和工作极限应变的卷积神经网络（CNN）的模型进行重复随机搜索。GAN使用相场法生成的DP钢微结构图像进行训练，CNN则利用DP钢微结构图像以及位错-晶体塑性有限元法计算的最大应力和工作极限应变进行训练。由于GAN潜变量降低了搜索空间维度，所构建的框架能够高效搜索微结构。该框架考虑了多种变形模式，从而可在复杂变形模式下探索所需微结构。利用所开发的框架，提出了一种具有细晶粒尺寸的微观结构。