Conference Tools for 2020 TMS Annual Meeting & Exhibition

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Meeting	2020 TMS Annual Meeting & Exhibition
Symposium	Characterization: Structural Descriptors, Data-Intensive Techniques, and Uncertainty Quantification
Presentation Title	Adversarial Networks for Microstructure Generation and Modeling Phase Transformation Kinetics
Author(s)	Wufei Ma, Elizabeth Jane Kautz, Arun Devaraj, Saumyadeep Jana, Vineet Joshi, Daniel Lewis, Bulent Yener
On-Site Speaker (Planned)	Wufei Ma
Abstract Scope	Micrograph quantification is essential to studying kinetics of phase transformations in several metallic systems. Machine learning has previously demonstrated success in image recognition tasks across several disciplines, however, sufficient image data available for model training is critical for success. In materials science studies, original image data can be limited, and is thus a hurdle to overcome when developing machine learning for microstructure image recognition tasks. Here, we develop a conditional generative adversarial network architecture (CGAN) for the purpose of generating synthetic microstructure images that can help when original image data is limited for phase transformation studies. In this work, a uranium-molybdenum alloy that undergoes a discontinuous precipitation reaction during thermo-mechanical processing is studied. We hypothesize that by understanding how synthetic images are processed by the CGAN we can gain insight into phase transformation kinetics, particularly what features of the microstructure image data is of most interest to the transformation process.
Proceedings Inclusion?	Planned: Supplemental Proceedings volume

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