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Micromagnetic materials characterization using machine learning
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DOI：10.1515/teme-2019-0099.png)
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Abstract
Micromagnetic materials characterization is a nondestructive means of predicting mechanical properties and stress of steel and iron products. The method is based on the circumstance that both mechanical and magnetic behaviour relate to microstructure over similar interaction mechanisms, which leads to characteristic correlations between mechanical and magnetic properties of ferromagnetic materials. The prediction of mechanical properties or stress from micromagnetic parameters represents an inverse problem commonly addressed by regression and classification approaches. Challenges for the industrial application of micromagnetic methods lie in the development of robust sensors, definition of significant features, and implementation of powerful machine learning algorithms for a reliable quantitative target value prediction by processing of the micromagnetic features. This contribution briefly explains the background of micromagnetics, describes the typical challenges experienced in practice and provides insight into latest progress in the application of machine learning to micromagnetic data.
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