  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.
