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Density-based clustering of crystal (mis)orientations and the orix Python library
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 orien...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
International Union of Crystallography
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534538/ https://www.ncbi.nlm.nih.gov/pubmed/33117110 http://dx.doi.org/10.1107/S1600576720011103 |
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author | Johnstone, Duncan N. Martineau, Ben H. Crout, Phillip Midgley, Paul A. Eggeman, Alexander S. |
author_facet | Johnstone, Duncan N. Martineau, Ben H. Crout, Phillip Midgley, Paul A. Eggeman, Alexander S. |
author_sort | Johnstone, Duncan N. |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-7534538 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | International Union of Crystallography |
record_format | MEDLINE/PubMed |
spelling | pubmed-75345382020-10-27 Density-based clustering of crystal (mis)orientations and the orix Python library Johnstone, Duncan N. Martineau, Ben H. Crout, Phillip Midgley, Paul A. Eggeman, Alexander S. J Appl Crystallogr Research Papers 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. International Union of Crystallography 2020-09-23 /pmc/articles/PMC7534538/ /pubmed/33117110 http://dx.doi.org/10.1107/S1600576720011103 Text en © Duncan N. Johnstone et al. 2020 http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution (CC-BY) Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original authors and source are cited.http://creativecommons.org/licenses/by/4.0/ |
spellingShingle | Research Papers Johnstone, Duncan N. Martineau, Ben H. Crout, Phillip Midgley, Paul A. Eggeman, Alexander S. Density-based clustering of crystal (mis)orientations and the orix Python library |
title | Density-based clustering of crystal (mis)orientations and the orix Python library |
title_full | Density-based clustering of crystal (mis)orientations and the orix Python library |
title_fullStr | Density-based clustering of crystal (mis)orientations and the orix Python library |
title_full_unstemmed | Density-based clustering of crystal (mis)orientations and the orix Python library |
title_short | Density-based clustering of crystal (mis)orientations and the orix Python library |
title_sort | density-based clustering of crystal (mis)orientations and the orix python library |
topic | Research Papers |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7534538/ https://www.ncbi.nlm.nih.gov/pubmed/33117110 http://dx.doi.org/10.1107/S1600576720011103 |
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