标题： 机器学习在多晶能散劳斑团簇中的应用 显示英文标题

标题： Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern

摘要： 我们通过将其表述为可以通过无监督机器学习（ML）解决的聚类问题，来解决能量色散劳厄衍射（EDLD）实验中晶粒对应的劳厄反射的识别问题。 为了可靠且高效地识别劳厄图案中的晶粒，我们采用了层次聚类（HC）和K均值聚类算法的组合。 这些算法使我们能够将相似的劳厄反射分组在一起，揭示衍射图案中潜在的晶粒结构。 此外，我们利用肘部法则来确定最佳聚类数量，以确保结果的准确性。 为了评估所提出方法的性能，我们使用了从镍线获得的模拟数据集和实验数据集进行了实验。 模拟数据集被生成以模仿真实世界EDLD实验的特征，而实验数据集则来自实际测量。 显示英文摘要

摘要： We address the identification of grain-corresponding Laue reflections in energy dispersive Laue diffraction (EDLD) experiments by formulating it as a clustering problem solvable through unsupervised machine learning (ML). To achieve reliable and efficient identification of grains in a Laue pattern, we employ a combination of clustering algorithms, namely hierarchical clustering (HC) and K-means. These algorithms allow us to group together similar Laue reflections, revealing the underlying grain structure in the diffraction pattern. Additionally, we utilise the elbow method to determine the optimal number of clusters, ensuring accurate results. To evaluate the performance of our proposed method, we conducted experiments using both simulated and experimental datasets obtained from nickel wires. The simulated datasets were generated to mimic the characteristics of real-world EDLD experiments, while the experimental datasets were obtained from actual measurements.