Texture is the orientation distribution of crystallites in polycrystalline materials. Given the discrete orientations, Schaeben suggested to adopt statistics for quantitative analysis of texture from discrete orientations, and he also conceived a clustering algorithm to facilitate the applications of statistical methods (H. Schaeben, J Appl Crystal 26:112–121, 1993). This data-driven scheme becomes more urgent and more necessary for the oncoming fourth paradigm: data-intensive scientific discovery, which follows after experimental science, theoretical science, and computational science paradigm. This research adopts a density-based clustering algorithm, DBSCAN, to process the orientation data from an austenitic stainless steel 316 L sample fabricated by selective laser melting. It is validated that the algorithm can robustly identify the orientation cluster (or texture component or preferred orientation). The statistical methods can successfully quantify the features of the identified orientation cluster with quantified uncertainty (statistical significance), which is often lacked in the general method of orientation distribution function. It is believed that this data-driven scheme can be applied to the many aspects of texture analysis.

中文翻译：

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一种数据驱动的纹理定量分析方案

纹理是多晶材料中微晶的取向分布。鉴于离散方向，Schaeben建议通过从离散方向质感的定量分析统计，并且他还构思了一个聚类算法，以方便统计方法（H. Schaeben，J申请水晶26的应用程序：112-121，1993年）。对于即将到来的第四个范式：数据密集型科学发现，紧随实验科学，理论科学和计算科学范式之后，这种由数据驱动的方案变得更加紧迫和必要。这项研究采用基于密度的聚类算法DBSCAN来处理通过选择性激光熔化制备的奥氏体不锈钢316 L样品的取向数据。验证了该算法可以可靠地识别方向簇（或纹理分量或首选方向）。统计方法可以用量化的不确定性（统计显着性）成功地量化所识别的取向簇的特征，这在取向分布函数的一般方法中通常是缺乏的。