Investigation on optimal microstructure of dual-phase steel with high strength and ductility by machine learning

TL;DR

This paper introduces an inverse analysis framework combining GAN and CNN to efficiently identify dual-phase steel microstructures with high strength and ductility, considering complex deformation modes.

Contribution

The study presents a novel inverse analysis method integrating GAN and CNN for microstructure design, enabling efficient exploration of microstructures with desired properties.

Findings

Proposed microstructure with fine grain size for high strength and ductility.

Efficient search enabled by low-dimensional latent space of GAN.

Framework considers multiple deformation modes for microstructure optimization.

Abstract

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.