Establishing process-structure linkages using Generative Adversarial Networks

TL;DR

This paper introduces a GAN-based method to synthesize material microstructures from processing conditions, enabling efficient exploration of process-structure relationships without extensive feature engineering.

Contribution

The study presents a novel GAN framework that generates microstructures directly from processing parameters, reducing domain knowledge requirements and broadening applicability.

Findings

High-fidelity microstructure synthesis achieved

Strong correlation between generated structures and processing conditions

Applicable across various material systems

Abstract

The microstructure of material strongly influences its mechanical properties and the microstructure itself is influenced by the processing conditions. Thus, establishing a Process-Structure-Property relationship is a crucial task in material design and is of interest in many engineering applications. We develop a GAN (Generative Adversarial Network) to synthesize microstructures based on given processing conditions. This approach is devoid of feature engineering, needs little domain awareness, and can be applied to a wide variety of material systems. Results show that our GAN model can produce high-fidelity multi-phase microstructures which have a good correlation with the given processing conditions.