Machine Learning for Microstructures Classification in Functional Materials

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The search for novel magnetic phases requires efficient quantitative microstructure analysis to extract microstructural information and to correlate it with its intrinsic magnetic parameters. Kerr micrographs of magnets hold vital information for analysing the distribution of grains and magnetic domain structures in the sample and when compared to the Electron Backscatter Diffraction (EBSD) approach for grain analysis, Kerr microscopy (KM) requires less time for sample preparation, image acquisition and material analysis. However, due to the complex microstructural features, it is not feasible to use traditional approaches of image analysis for extracting this information. In this paper, we have developed a robust and time-efficient deep learning-based model for the extraction of microstructural information in the NdFeB sintered permanent magnets from Kerr microscopy images with high accuracy and compared its performance with EBSD output and with manually hand-labelled dataset prepared by a subject expert.

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Journal: TechConnect Briefs
Volume: TechConnect Briefs 2021
Published: October 18, 2021
Pages: 114 - 117
Industry sectors: Advanced Materials & Manufacturing | Medical & Biotech
Topics: Advanced Manufacturing, Materials Characterization & Imaging
ISBN: 978-0-578-99550-2