| Autor: |
Peng Rong, Fengguo Zhang, Qing Yang, Han Chen, Qiwei Shi, Shengyi Zhong, Zhe Chen, Haowei Wang |
| Jazyk: |
angličtina |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Materials, Vol 15, Iss 4, p 1502 (2022) |
| Druh dokumentu: |
article |
| ISSN: |
1996-1944 |
| DOI: |
10.3390/ma15041502 |
| Popis: |
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. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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