Abstract
Abstract In our energy-consuming society, understanding magnetization reversal in permanent magnets is crucial for improving energy conversion efficiency between electric energy and mechanical energy. First-order reversal curves (FORCs) have enabled qualitative studies of reversal mechanisms, however, further understanding based on quantitative analysis is still difficult due to the complexity of FORC diagrams. We introduce a machine-learning based approach combining the Gaussian mixture model and the Davies-Bouldin index to separate characteristic features in FORC diagrams of Nd-Fe-B magnets. The clustering method is evaluated using several FORC diagrams obtained at different temperatures, and we also demonstrated hysteresis loop reconstruction using the clustered FORC diagrams.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
