Density-based clustering of crystal (mis)orientations and the orix Python library
© Duncan N. Johnstone et al. 2020.
| Veröffentlicht in: | Journal of applied crystallography. - 1998. - 53(2020), Pt 5 vom: 01. Okt., Seite 1293-1298 |
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| 1. Verfasser: | |
| Weitere Verfasser: | , , , |
| Format: | Online-Aufsatz |
| Sprache: | English |
| Veröffentlicht: |
2020
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| Zugriff auf das übergeordnete Werk: | Journal of applied crystallography |
| Schlagworte: | Journal Article Python computer programs crystal orientations data clustering fundamental zones |
| Zusammenfassung: | © Duncan N. Johnstone et al. 2020. Crystal orientation mapping experiments typically measure orientations that are similar within grains and misorientations that are similar along grain boundaries. Such (mis)orientation data cluster in (mis)orientation space, and clusters are more pronounced if preferred orientations or special orientation relationships are present. Here, cluster analysis of (mis)orientation data is described and demonstrated using distance metrics incorporating crystal symmetry and the density-based clustering algorithm DBSCAN. Frequently measured (mis)orientations are identified as corresponding to similarly (mis)oriented grains or grain boundaries, which are visualized both spatially and in three-dimensional (mis)orientation spaces. An example is presented identifying deformation twinning modes in titanium, highlighting a key application of the clustering approach in identifying crystallographic orientation relationships and similarly oriented grains resulting from specific transformation pathways. A new open-source Python library, orix, that enabled this work is also reported |
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| Beschreibung: | Date Revised 29.03.2024 published: Electronic-eCollection Citation Status PubMed-not-MEDLINE |
| ISSN: | 0021-8898 |
| DOI: | 10.1107/S1600576720011103 |