First World Congress on Artificial Intelligence in Materials and Manufacturing (AIM 2022): Wednesday Plenary
Program Organizers: Taylor Sparks, University of Utah; Michael Dawson-Haggerty, Kerfed, Inc.; Elizabeth Holm, University of Michigan; Jin Kocsis, Purdue University; Adam Kopper, Mercury Marine; Benji Maruyama; James Warren, National Institute of Standards and Technology
Wednesday 8:00 AM
April 6, 2022
Room: William Penn Ballroom
Location: Omni William Penn Hotel
Session Chair: Taylor Sparks, University of Utah
8:00 AM Plenary
Generating Realistic Material Microstructures Using Conditional GANs for Advanced Manufacturing: Scott Howland1; Kevin Loew1; Henry Kvinge1; Xiaolong Ma1; Joshua Silverstein1; Nicole Overman1; Md. Reza E Rabby1; Scott Taysom1; Tianhao Wang1; WoongJo Choi1; Scott Whalen1; Keerti Kappagantula1; Luke Gosink1; Tegan Emerson; 1Pacific Northwest National Laboratory
Advanced manufacturing technologies enable material scientists to produce materials with increasingly attractive physical properties. Because they are novel, intuitions for what experimental parameters produce superior materials don’t necessarily transfer from older processes. And since advanced processes are commonly limited in scale, it is cost and time-prohibitive to conduct extensive research into how experimental parameters influence production outcomes. A machine learning pipeline used to extrapolate from a limited set of experiments could help practitioners judge what future experiments might yield the best-performing materials, accelerating innovation and decreasing cost. As a step towards such a pipeline, we explore the use of generative adversarial networks (GANs) conditioned on practitioner-specified experimental process parameters to generate plausible microstructure imagery. We show that GANs are able to produce realistic microstructures for a variety of temper conditions, propose next steps for incorporating more conditioning information, and show how this approach could fit into an end-to-end scientific pipeline.
8:45 AM Break