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Deep learning and correlative microscopy for quantification of grain orientation in sintered FeNdB-type permanent magnets by domain pattern analysis

  • Based on a data-driven approach, a computer-assisted workflow for the quantitative analysis of optical Kerr microscopy images of sintered FeNdB-type permanent magnets was developed. By analyzing the domain patterns visible in the Kerr image with data-driven approaches such as traditional machine learning and advanced deep learning, we can quantify grain orientation and size with a better trade-off between accuracy and higher throughput than electron backscatter diffraction (EBSD). The key distinction between traditional machine learning and advanced deep learning lies in feature extraction. Traditional methods require manual, user-dependent feature extraction from input data, while advanced deep learning achieves this automatically. The predictions from the trained models were compared to the measurements from EBSD for performance evaluation. The proposed data-driven model is trained on the dataset created from the correlative microscopy technique, which requires the images of grains extracted from the Kerr microscopy and corresponding EBSD grain orientation data (Euler angles). The fine-tuned deep learning model shows better generalization ability than the traditional machine learning models trained on the manually extracted features and resulted in a mean absolute error of less than 5° for grain orientation of the anisotropic magnet samples when evaluated against the measured EBSD values. The developed approach has reduced the measurement effort for grain orientation by 5 times and have sufficient accuracy when compared to the EBSD.

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Metadaten
Author:Amit Kumar ChoudharyORCiD, Tvrtko Grubesa, Andreas Jansche, Timo BernthalerORCiD, Dagmar Goll, Gerhard Schneider
DOI:https://doi.org/10.1016/j.actamat.2023.119563
Source Title (English):Acta Materialia
Document Type:Article
Language:English
Year of Completion:2024
Release Date:2024/01/30
Volume:264
Article Number:119563
Number of Pages:12
Faculty:Maschinenbau und Werkstofftechnik
Open Access:Open Access
Licence (German):License LogoCreative Commons - CC BY - Namensnennung 4.0 International