|About this Abstract
||2022 TMS Annual Meeting & Exhibition
||AI/Data Informatics: Computational Model Development, Validation, and Uncertainty Quantification
||One-stage Simulation of EBSD Patterns over Multiple Parameters through a CVAE-GAN Model
||Zihao Ding, Marc De Graef
|On-Site Speaker (Planned)
Currently, the mainstream approach for electron backscatter diffraction (EBSD) pattern simulation is through a physics-based forward model, which first computes the back-scattered yield over all directions, and then generates patterns corresponding to certain orientations through a gnomonic projection. The first stage is time-consuming, limiting its application when there is variation in parameters other than orientation. For discriminative purposes, the EBSD-CNN and EBSDDI-CNN approaches have proved great feature extraction capability of deep neural networks in this domain. Recently, we have shown that a conditional variational autoencoder (CVAE) can realize parametric simulation of EBSD patterns. As a preliminary verification, it takes orientation as the only variable input. In this study, the model is combined with a generative adversarial network (GAN) to realize EBSD pattern simulation over multiple parameters. Compared with the conventional forward model, the deep generative model summarizes the distribution of back-scattered electrons at a higher level.
||Machine Learning, Characterization, Computational Materials Science & Engineering