|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||Austenitic Parent Grain Reconstruction in Martensitic Steel Using Deep Learning
||Patxi Fernandez-Zelai, Andrés Márquez Rossy, Quinn Campbell, Andrzej Nycz, Michael M Kirka
|On-Site Speaker (Planned)
Phase transformations take place in many structural materials following solidification. Phase reconstruction algorithms are commonly used to infer the underlying parent phase crystal structure from spatial-orientation patterns present in electron backscatter diffraction micrographs. In the past decade machine learning methods have been shown to perform exceptionally well in a number of vision tasks. In this work we develop a deep convolutional architecture for estimating prior austenite micrographs from observed martensite electron backscatter diffraction micrographs. Efficient learning of the orientation relationships within the network is facilitated by a novel data augmentation strategy. Training is performed using only four micrographs by exploiting the arbitrariness of the reference sample coordinate system. Model inference is much faster than algorithmic approaches and generalizes well when applied to micrographs of a completely different material. This work demonstrates that modern computer vision models, trained with only a few micrographs, are well suited for analyzing orientation imaging micrographs.
||Machine Learning, Modeling and Simulation, Phase Transformations