Generating reduced-order, synthetic grain structure datasets that accurately represent the measured grain structure of a material is important for reducing the cost and increasing the accuracy of computational crystal plasticity efforts. This study introduces a machine-learning-based approach, termed texture adaptive clustering and sampling (TACS), for generating representative Euler angle datasets that accurately mimic the crystallographic texture. The TACS approach employs K-means clustering and density-based sampling in a closed-loop iteration to create representative Euler angle datasets. Proof-of-principle experiments were performed on rolled and recrystallized low-carbon steel. Validation of the TACS approach was extended to twenty-two datasets, varying lattice structures, and complex crystallographic textures, thereby encompassing a broad range of materials and crystal structures. Kolmogorov-Smirnov (K-S) test comparisons underscore the performance of the TACS approach over traditional electron backscatter diffraction EBSD dataset reduction techniques, with average K-S test scores nearing 0.9, indicating a high-fidelity representation of the original datasets. In contrast, conventional methods display scores below 0.3, indicating less reliability of the structure representation. The independence of the TACS approach from material texture and its capability to autonomously generate datasets with predetermined data points demonstrates its unbiased potential in streamlining dataset preparation for crystallographic analysis.

生成准确表示测量的材料晶粒结构的降阶合成晶粒结构数据集对于降低成本和提高计算晶体塑性工作的准确性非常重要。这项研究引入了一种基于机器学习的方法，称为纹理自适应聚类和采样（TACS），用于生成准确模仿晶体纹理的代表性欧拉角数据集。 TACS 方法在闭环迭代中采用 K 均值聚类和基于密度的采样来创建具有代表性的欧拉角数据集。对轧制和再结晶低碳钢进行了原理验证实验。 TACS 方法的验证扩展到 22 个数据集、不同的晶格结构和复杂的晶体结构，从而涵盖了广泛的材料和晶体结构。 Kolmogorov-Smirnov (K-S) 测试比较强调了 TACS 方法相对于传统电子背散射衍射 EBSD 数据集缩减技术的性能，平均 K-S 测试分数接近 0.9，表明原始数据集的高保真度表示。相比之下，传统方法显示的分数低于 0.3，表明结构表示的可靠性较低。 TACS 方法独立于材料纹理及其自动生成具有预定数据点的数据集的能力，证明了其在简化晶体学分析数据集准备方面的公正潜力。