About this Abstract |
Meeting |
MS&T22: Materials Science & Technology
|
Symposium
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AI for Big Data Problems in Advanced Imaging, Materials Modeling and Automated Synthesis
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Presentation Title |
Neural Network Prediction of Dynamical Electron Back-Scattered Diffraction Patterns Based on Kinematical Patterns |
Author(s) |
Clement Lafond, Marc De Graef |
On-Site Speaker (Planned) |
Clement Lafond |
Abstract Scope |
Dictionary indexing (DI) of Electron Back-Scattered Diffraction (EBSD) patterns has shown to overcome issues of commercial Hough Indexing (i.e., lack of robustness against pattern noise) using a comparison between experimental and simulated EBSD patterns. The simulated patterns are computed from dynamical simulations leading to high computation time (up to days or weeks) for complex crystal phases such as precipates in metal alloys or low symmetry geological structures. We propose a new approach to predict dynamical EBSD simulation based on a fast kinematical simulation using Image-to-Image Translation Generative Adversarial Networks (GAN). The accuracy of model, and computation time are evaluated for different crystal structures. The results suggest that such GANs can predict accurate EBSD patterns while reducing the computation time by several orders of magnitude. |