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 convolu
tional 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 microstruc
tures, 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 mi
crostructures 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 re
quired microstructures to be explored under complex deformation modes. A microstructure
with a fine grain size was proposed by using the developed framework.