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...
| Main Authors: | , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
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2022
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| Online Access: | https://hdl.handle.net/10356/160594 |
| _version_ | 1826110086980829184 |
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| author | Wittwer, Mallory Seita, Matteo |
| author2 | School of Mechanical and Aerospace Engineering |
| author_facet | School of Mechanical and Aerospace Engineering Wittwer, Mallory Seita, Matteo |
| author_sort | Wittwer, Mallory |
| collection | NTU |
| 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. |
| first_indexed | 2024-10-01T02:29:03Z |
| format | Journal Article |
| id | ntu-10356/160594 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:29:03Z |
| publishDate | 2022 |
| record_format | dspace |
| spelling | ntu-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 |
| spellingShingle | Engineering::Mechanical engineering Crystal Orientation Machine Learning Wittwer, Mallory Seita, Matteo A machine learning approach to map crystal orientation by optical microscopy |
| title | 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_short | A machine learning approach to map crystal orientation by optical microscopy |
| title_sort | machine learning approach to map crystal orientation by optical microscopy |
| topic | Engineering::Mechanical engineering Crystal Orientation Machine Learning |
| url | https://hdl.handle.net/10356/160594 |
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