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

TL;DR

This paper presents a machine learning approach using hierarchical clustering and K-means to identify and group Laue reflections by grain in energy dispersive Laue diffraction patterns, improving grain analysis accuracy.

Contribution

It introduces an unsupervised machine learning method combining clustering algorithms and the elbow method for reliable grain identification in Laue diffraction patterns.

Findings

Effective clustering of Laue reflections in simulated datasets.

Successful application to experimental nickel wire data.

Improved grain structure visualization in diffraction patterns.

Abstract

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.