  The research and development cycle of advanced manufacturing processes
traditionally requires a large investment of time and resources. Experiments
can be expensive and are hence conducted on relatively small scales. This poses
problems for typically data-hungry machine learning tools which could otherwise
expedite the development cycle. We build upon prior work by applying
conditional generative adversarial networks (GANs) to scanning electron
microscope (SEM) imagery from an emerging manufacturing process, shear assisted
processing and extrusion (ShAPE). We generate realistic images conditioned on
temper and either experimental parameters or material properties. In doing so,
we are able to integrate machine learning into the development cycle, by
allowing a user to immediately visualize the microstructure that would arise
from particular process parameters or properties. This work forms a technical
backbone for a fundamentally new approach for understanding manufacturing
processes in the absence of first-principle models. By characterizing
microstructure from a topological perspective we are able to evaluate our
models' ability to capture the breadth and diversity of experimental scanning
electron microscope (SEM) samples. Our method is successful in capturing the
visual and general microstructural features arising from the considered
process, with analysis highlighting directions to further improve the
topological realism of our synthetic imagery.
