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Title: A machine learning approach to map crystal orientation by optical microscopy
Authors: Wittwer, Mallory
Seita, Matteo
Keywords: Engineering::Mechanical engineering
Issue Date: 2022
Source: Wittwer, M. & Seita, M. (2022). A machine learning approach to map crystal orientation by optical microscopy. Npj Computational Materials, 8(1), 8-.
Project: MOE2017-T2-2-119
Journal: npj Computational Materials
Abstract: Mapping grain orientation in crystalline solids is essential to investigate the relationships between local microstructure and crystallography and interpret materials properties. One of the main techniques used to perform these studies is electron backscatter diffraction (EBSD). Due to the limited measurement throughput, however, EBSD is not suitable for characterizing samples with long-range microstructure heterogeneity, nor for building large material libraries that include numerous specimens. We present a machine learning approach for high-throughput crystal orientation mapping, which relies on the optical technique called directional reflectance microscopy. We successfully apply our method on Inconel 718 specimens produced by additive manufacturing, which exhibit complex, spatially-varying microstructures. These results demonstrate that optical orientation mapping on a metal alloy is achievable. Since our method is data-driven, it can be easily extended to different alloy systems produced using different manufacturing processes.
ISSN: 2057-3960
DOI: 10.1038/s41524-021-00688-1
Schools: School of Mechanical and Aerospace Engineering 
School of Materials Science and Engineering 
Rights: © 2022 The Author(s). This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons. org/licenses/by/4.0/.
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:MAE Journal Articles
MSE Journal Articles

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