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- Title
Processing Laue Microdiffraction Raster Scanning Patterns with Machine Learning Algorithms: A Case Study with a Fatigued Polycrystalline Sample.
- Authors
Rong, Peng; Zhang, Fengguo; Yang, Qing; Chen, Han; Shi, Qiwei; Zhong, Shengyi; Chen, Zhe; Wang, Haowei
- Abstract
The massive amount of diffraction images collected in a raster scan of Laue microdiffraction calls for a fast treatment with little if any human intervention. The conventional method that has to index diffraction patterns one-by-one is laborious and can hardly give real-time feedback. In this work, a data mining protocol based on unsupervised machine learning algorithm was proposed to have a fast segmentation of the scanning grid from the diffraction patterns without indexation. The sole parameter that had to be set was the so-called "distance threshold" that determined the number of segments. A statistics-oriented criterion was proposed to set the "distance threshold". The protocol was applied to the scanning images of a fatigued polycrystalline sample and identified several regions that deserved further study with, for instance, differential aperture X-ray microscopy. The proposed data mining protocol is promising to help economize the limited beamtime.
- Subjects
MACHINE learning; DATA mining; DIFFRACTION patterns
- Publication
Materials (1996-1944), 2022, Vol 15, Issue 4, p1502
- ISSN
1996-1944
- Publication type
Article
- DOI
10.3390/ma15041502