Density-based clustering of crystal orientations and misorientations and the orix python library

TL;DR

This paper introduces a method for clustering crystal orientations and misorientations using density-based algorithms, enhanced by symmetry-aware distance metrics, and presents the open-source orix Python library for practical implementation.

Contribution

It develops a symmetry-aware clustering approach for (mis)orientation data and introduces the open-source orix Python library for this purpose.

Findings

Effective identification of grains and boundaries in orientation data.

Visualization of clusters in spatial and 3D misorientation spaces.

Open-source availability of the orix library facilitates adoption.

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 will 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 grains, grain boundaries or orientation relationships, which are visualised both spatially and in three-dimensional (mis)orientation spaces. A new open-source python library, orix, is also reported.