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

Authors: Amir Tosson; Mohammad Shokr; Mahmoud Al Humaidi; Eduard Mikayelyan; Christian Gutt; Ulrich Pietsch

Addresses: Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany ' Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany ' Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany ' Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany ' Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany ' Department of Physics, The University of Siegen, Walter-Flex-Str. 3, 57072, Siegen, Germany

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.

Keywords: machine learning; Laue diffraction; X-ray; hierarchical clustering; K-means; crystallography; artificial intelligence; synchrotron radiation; polycrystalline material; energy dispersive detection; elbow method; unsupervised machine learning; grain identification; CCD cameras; reciprocal space; high brilliance X-ray.

DOI: 10.1504/IJAISC.2024.145610

International Journal of Artificial Intelligence and Soft Computing, 2024 Vol.8 No.3, pp.173 - 194

Received: 25 May 2023
Accepted: 31 Jan 2024
Published online: 09 Apr 2025 *

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