  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.
