Establishing process-structure linkages using Generative Adversarial Networks
This addresses the need for efficient material design in engineering applications, but it is incremental as it applies an existing GAN method to a new domain.
The paper tackled the problem of establishing process-structure relationships in material design by developing a Generative Adversarial Network (GAN) to synthesize microstructures based on processing conditions, resulting in high-fidelity multi-phase microstructures with good correlation to given conditions.
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.