论文标题
基于密度的晶体取向和不良方向的聚类和Orix Python库
Density-based clustering of crystal orientations and misorientations and the orix python library
论文作者
论文摘要
晶体取向映射实验通常测量在晶粒边界上相似的晶粒和不良方向中相似的方向。这种（MIS）取向数据将集中在（MIS）取向空间中，如果存在首选方向或特殊方向关系，则群集更为明显。在这里，使用结合晶体对称性和基于密度的聚类算法DBSCAN的距离指标来描述和证明（MIS）取向数据的聚类分析。经常测量的（MIS）方向被确定为对应于晶粒，晶界或方向关系，它们在空间和三维（MIS）取向空间中都可以看到。还报道了一个新的开源Python图书馆Orix。
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.
