Journal
ACTA MATERIALIA
Volume 170, Issue -, Pages 118-131Publisher
PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.actamat.2019.03.026
Keywords
Microstructure reconstruction; Convolutional neural network; Electron backscatter diffraction
Funding
- U.S. Department of Energy through the Los Alamos National Laboratory
- National Nuclear Security Administration of U.S. Department of Energy [89233218CNA000001]
- Los Alamos National Laboratory's Momentum Laboratory Directed Research and Development (LDRD) Reserve Project [20180677ER]
- LDRD-ECR project [20190571ECR]
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Electron backscatter diffraction (EBSD) is the most commonly used technique for obtaining spatially resolved microstructural information from polycrystalline materials. We have developed two convolutional neural network approaches based on domain transform and transfer learning to reconstruct crystal orientations from electron backscatter diffraction patterns. Our models are robust to experimentally measured image noise and index orientations as fast as the highest EBSD scanning rates. We demonstrate that the quaternion norm metric is a strong indicator for assessing the reliability of the reconstructions in the absence of the ground truth. We demonstrate the applicability of the current methods on a tantalum sample. (C) 2019 Acta Materialia Inc. Published by Elsevier Ltd. All rights reserved.
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