  With a goal of accelerating fabrication of additively manufactured components
with precise microstructures, we developed a method for structural
characterization of key features in additively manufactured materials and
parts. The method utilizes deep learning based on an image-to-image translation
conditional Generative Adversarial Neural Network architecture and enables fast
and incrementally more accurate predictions of the prevalent geometric
features, including melt pool boundaries and printing induced defects visible
in etched optical images. These structural details are heterogeneous in nature.
Our method specifies the microstructure state of an additive built via
statistical distribution of structural details, based on an ensemble of
collected images. Extensions of the method are proposed to address Artificial
Intelligence implementation of developed machine learning model for in real
time control of additive manufacturing.
