A Conditional Generative Model for Predicting Material Microstructures from Processing Methods

TL;DR

This paper introduces a deep learning model that predicts material microstructures from processing conditions by generating realistic microstructure images conditioned on processing parameters, aiding material design.

Contribution

It develops an auxiliary classifier Wasserstein GAN with gradient penalty (ACWGAN-GP) for conditional microstructure synthesis, reducing the need for feature engineering and domain expertise.

Findings

ACWGAN-GP effectively synthesizes microstructures conditioned on processing methods.

The model produces high-quality multiphase microstructure images.

Demonstrated on UHCS data with accurate representation of cooling effects.

Abstract

Microstructures of a material form the bridge linking processing conditions - which can be controlled, to the material property - which is the primary interest in engineering applications. Thus a critical task in material design is establishing the processing-structure relationship, which requires domain expertise and techniques that can model the high-dimensional material microstructure. This work proposes a deep learning based approach that models the processing-structure relationship as a conditional image synthesis problem. In particular, we develop an auxiliary classifier Wasserstein GAN with gradient penalty (ACWGAN-GP) to synthesize microstructures under a given processing condition. This approach is free of feature engineering, requires modest domain knowledge and is applicable to a wide range of material systems. We demonstrate this approach using the ultra high carbon steel (UHCS) database, where each microstructure is annotated with a label describing the cooling method it was subjected to. Our results show that ACWGAN-GP can synthesize high-quality multiphase microstructures for a given cooling method.