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Data Science Approach for EBSD Data Processing and Materials Design for Magnesium Alloy
Electron backscatter diffractionElectron Backscatter Diffraction (EBSD) method is widely adopted in metal fields. However, despite the abundant data sources, sufficient analysis covering all features is often absent. Especially with the emerging in-situ techniques, data processingData processing is time-consuming, where access to every bit of data is imperative. In this work, a toolkit is developed with the aim of processing EBSDElectron Backscatter Diffraction (EBSD) data automatically and efficiently. Two parts of toolkits are developed with Matlab and Mtex. One is used to correlate two maps, with simple implementation, results will generate within few minutes, indicating the grains correlation between two maps. The other correlates a series of in-situ datasets, making each individual grain become trackable. With the assistance of the toolkits, a large dataset containing pixels, digital information, and grains properties through an in-situ process can be created. Thus, the microfeatures and grain behaviors are studied using novel data science methods, especially machine learning and deep learning.
Data Science Approach for EBSD Data Processing and Materials Design for Magnesium Alloy
Electron backscatter diffractionElectron Backscatter Diffraction (EBSD) method is widely adopted in metal fields. However, despite the abundant data sources, sufficient analysis covering all features is often absent. Especially with the emerging in-situ techniques, data processingData processing is time-consuming, where access to every bit of data is imperative. In this work, a toolkit is developed with the aim of processing EBSDElectron Backscatter Diffraction (EBSD) data automatically and efficiently. Two parts of toolkits are developed with Matlab and Mtex. One is used to correlate two maps, with simple implementation, results will generate within few minutes, indicating the grains correlation between two maps. The other correlates a series of in-situ datasets, making each individual grain become trackable. With the assistance of the toolkits, a large dataset containing pixels, digital information, and grains properties through an in-situ process can be created. Thus, the microfeatures and grain behaviors are studied using novel data science methods, especially machine learning and deep learning.
Data Science Approach for EBSD Data Processing and Materials Design for Magnesium Alloy
The Minerals, Metals & Materials Series
Leonard, Aeriel (editor) / Barela, Steven (editor) / Neelameggham, Neale R. (editor) / Miller, Victoria M. (editor) / Tolnai, Domonkos (editor) / Yi, Haoran (author) / Zeng, Xun (author) / Guan, Dikai (author)
TMS Annual Meeting & Exhibition ; 2024 ; Orlando, FL, USA
2024-02-03
5 pages
Article/Chapter (Book)
Electronic Resource
English
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