Application of machine learning in grain-related clustering of Laue spots in a polycrystalline energy dispersive Laue pattern
This is an incremental improvement for materials science researchers analyzing polycrystalline structures, as it applies existing clustering methods to a specific domain problem.
The paper tackled the problem of identifying grain-corresponding Laue reflections in energy dispersive Laue diffraction experiments by formulating it as a clustering problem and using hierarchical clustering and K-means, achieving reliable grain identification as validated on simulated and experimental datasets from nickel wires.
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.