Classification of EBSD Kikuchi Patterns for Stainless Steel by Unsupervised Learning Methods to Investigate Grain Boundaries

• Published in: E-journal of surface science and nanotechnology Vol. 21; no. 3; pp. 128 - 131
• Main Authors: Aoyagi, Satoka, Hayashi, Daisuke, Murase, Yoshiharu, Miyauchi, Naoya, Itakura, Akiko N.
• Format: Journal Article
• Language: English
• Published: Tokyo The Japan Society of Vacuum and Surface Science 25-02-2023; Japan Science and Technology Agency
• Subjects: Crystal lattices; Crystal structure; Diffraction patterns; Dislocations; Electron backscatter diffraction; Grain boundaries; Hydrogen; Indexing; Kikuchi bands; Mixed crystals; Multivariate curve resolution; Pixels; Principal component analysis; Principal components analysis; Stainless steel; Stainless steels; Steel; Structural analysis; Teaching methods; Unsupervised learning
• ISSN: 1348-0391; 1348-0391
• DOI: 10.1380/ejssnt.2023-023

Electron backscatter diffraction (EBSD) indexing based on Kikuchi diffraction patterns, which indicate the types and orientation of the crystal lattice, is effective for characterizing crystals. Most regions in a sample can be indexed due to simulation of diffraction patterns of possible crystal types, orientations, and angles. However, indexing some of the complex regions related to the grain boundaries, dislocations, and strain areas is difficult. Moreover, minor crystal structures are possibly omitted from the index results. To characterize all the regions, including such complicated boundaries, the analysis of raw data, including all Kikuchi patterns, is necessary. By analyzing all the Kikuchi patterns, significant information can be extracted from mixed crystal conditions. Stainless steel was used as the model sample in this study. As hydrogen diffusion in metals strongly depends on the crystal structure and grain boundaries, structural analysis is required to study hydrogen behavior in steel. In this study, all Kikuchi patterns at all pixels in a measurement area of stainless steel were analyzed simultaneously using unsupervised learning methods, such as principal component analysis and multivariate curve resolution, and the pixels of the measurement area were classified based on the Kikuchi patterns to investigate the grain boundaries and dislocations in detail.