A machine learning approach to map crystal orientation by optical microscopy

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 m...

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Main Authors: Wittwer, Mallory, Seita, Matteo
Other Authors: School of Mechanical and Aerospace Engineering
Format: Article
Language:English
Published: 2022
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Online Access:https://hdl.handle.net/10356/160594
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Institution: Nanyang Technological University
Language: English
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spelling sg-ntu-dr.10356-1605942022-07-27T05:58:44Z A machine learning approach to map crystal orientation by optical microscopy Wittwer, Mallory Seita, Matteo School of Mechanical and Aerospace Engineering School of Materials Science and Engineering Engineering::Mechanical engineering Crystal Orientation Machine Learning 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. Ministry of Education (MOE) Published version This research was funded by the Ministry of Education of Singapore, Official Number: MOE2017-T2-2-119 2022-07-27T05:58:44Z 2022-07-27T05:58:44Z 2022 Journal Article Wittwer, M. & Seita, M. (2022). A machine learning approach to map crystal orientation by optical microscopy. Npj Computational Materials, 8(1), 8-. https://dx.doi.org/10.1038/s41524-021-00688-1 2057-3960 https://hdl.handle.net/10356/160594 10.1038/s41524-021-00688-1 2-s2.0-85123197864 1 8 8 en MOE2017-T2-2-119 npj Computational Materials © 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/. application/pdf
institution Nanyang Technological University
building NTU Library
continent Asia
country Singapore
Singapore
content_provider NTU Library
collection DR-NTU
language English
topic Engineering::Mechanical engineering
Crystal Orientation
Machine Learning
spellingShingle Engineering::Mechanical engineering
Crystal Orientation
Machine Learning
Wittwer, Mallory
Seita, Matteo
A machine learning approach to map crystal orientation by optical microscopy
description 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.
author2 School of Mechanical and Aerospace Engineering
author_facet School of Mechanical and Aerospace Engineering
Wittwer, Mallory
Seita, Matteo
format Article
author Wittwer, Mallory
Seita, Matteo
author_sort Wittwer, Mallory
title A machine learning approach to map crystal orientation by optical microscopy
title_short A machine learning approach to map crystal orientation by optical microscopy
title_full A machine learning approach to map crystal orientation by optical microscopy
title_fullStr A machine learning approach to map crystal orientation by optical microscopy
title_full_unstemmed A machine learning approach to map crystal orientation by optical microscopy
title_sort machine learning approach to map crystal orientation by optical microscopy
publishDate 2022
url https://hdl.handle.net/10356/160594
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