Density-based clustering of crystal (mis)orientations and the orix Python library.

Duncan N Johnstone,Ben H Martineau,Phillip Crout,Paul A Midgley,Alexander S Eggeman

doi:10.1107/s1600576720011103

Duncan N Johnstone, Ben H Martineau + Show 3 more

Open Access

https://doi.org/10.1107/s1600576720011103

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Abstract

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.

Highlights

IntroductionThe distribution of crystal orientations in a polycrystalline material (i.e. crystallographic texture) and characteristic misorientations between neighbouring crystals (i.e. orientation relationships) are affected by material processing and influence material properties (Kocks et al, 1998; Sutton &Baluffi, 2007)
The distribution of crystal orientations in a polycrystalline material and characteristic misorientations between neighbouring crystals are affected by material processing and influence material properties (Kocks et al, 1998; Sutton &Baluffi, 2007)
We report on density-based clustering oforientations in the presence of crystal symmetry and establish an open-source Python library, named orix, for handling crystalorientation data

Summary

Introduction

The distribution of crystal orientations in a polycrystalline material (i.e. crystallographic texture) and characteristic misorientations between neighbouring crystals (i.e. orientation relationships) are affected by material processing and influence material properties (Kocks et al, 1998; Sutton &Baluffi, 2007). Measuring the local crystal orientation throughout a material is common in modern materials characterization Such mapping is usually achieved using scanning diffraction techniques such as electron backscatter diffraction (EBSD) (Schwartz, 2009), scanning electron diffraction (Zaefferer, 2000; Rauch et al, 2008) and X-ray microLaue diffraction (Ice & Pang, 2009). These techniques use a small (nm–mm) probe to address numerous locations across the specimen while recording diffraction data at each position. Since crystal orientations and misorientations are both described as passive rotations in three dimensions, they can be represented and analysed provided that crystal symmetry is treated appropriately

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