Presentation Information
[20p-C601-7][Young Scientist Presentation Award Speech] Development of real-time and high-speed magnetic domain measurement system for iron loss analysis and Application of machine learning
〇Ryunosuke Nagaoka1, Ken Masuzawa1, Alexandre Lira Foggiatto1, Chiharu Mitsumata1, Takahiro Yamazaki1, Ippei Obayashi2, Yasuaki Hiraoka3, Masato Kotsugi1 (1.Tokyo Univ. of Science, 2.Okayama Univ., 3.Kyoto Univ.)
Keywords:
materials informatics,iron loss,machine learning
With the global spread of electric vehicles, there is an urgent need to develop magnetic materials for motors with low iron loss. Our group has applied the "extended Landau theory (ex-GL)" utilizing machine learning to magnetic domain structures to analyze the mechanism of coercivity in quasi-static processes. In this study, we aimed to analyze the mechanism of iron loss under AC magnetic field. We consistently developed a measurement system for dynamic magnetic domain structure and extracted features based on the ex-GL.