Extracting local nucleation fields in permanent magnets using machine learning

Markus Gusenbauer,Panpan Zhao,Johann Fischbacher,Alexander Kovacs,Thomas Schrefl,Thomas George Woodcock,Harald Oezelt

doi:10.1038/s41524-020-00361-z

Abstract

Microstructural features play an important role in the quality of permanent magnets. The coercivity is greatly influenced by crystallographic defects, like twin boundaries, as is well known for MnAl-C. It would be very useful to be able to predict the macroscopic coercivity from microstructure imaging. Although this is not possible now, in the present work we examine a related question, namely the prediction of simulated nucleation fields of a quasi-three-dimensional (rescaled and extruded) system constructed from a two-dimensional image. We extract features of the image and analyze them via machine learning. A large number of extruded systems are constructed from 10 × 10 pixel sub-images of an Electron Backscatter Diffraction (EBSD) image using an automated meshing procedure. A local nucleation field is calculated by micromagnetic simulation of each quasi-three-dimensional system. Decision trees, trained with the simulation results, can predict nucleation fields of these quasi-three-dimensional systems from new images within seconds. As for now we cannot quantitatively predict the macroscopic coercivity, nevertheless we can identify weak spots in the magnet and see trends in the nucleation field distribution.

Highlights

Permanent magnets are of great interest in today’s economy
(2) Partial dependence plots show the relation from features to Alternatively to the simulation of single large systems, several small parts, each covering a specific aspect of the microstructure, can be simulated
A promising approach to reduce the computational costs is the In the end we suggest a way to use the predicted nucleation fields use of machine learning to predict the coercivity of permanent for obtaining bulk magnetic properties. magnets[18]

Summary

Introduction

Permanent magnets are of great interest in today’s economy. Green energy applications, such as in wind turbines and hybrid/electric vehicles demand high-performance permanent magnets. The performance of permanent magnets is mainly determined by intrinsic magnetic properties and microstructural features. The intrinsic properties are adjusted by including rare earth (RE) elements, which mainly increase the magnetocrystalline anisotropy. MnAlC contains no RE or other critical raw materials and its intrinsic magnetic properties make it an attractive alternative to certain types of RE-based permanent magnets[2]. The microstructure of MnAl-C magnets is known to contain a range of defects, such as grain boundaries, twins, and antiphase boundaries[3,4,5,6,7]. Kronmüller and Goll discussed microstructural properties of advanced hard magnetic materials and its relation to the quality of permanent magnets[8]. By investigating the various boundary types of MnAl-C, their distribution and the effect on the coercivity by micromagnetic simulations and machine learning, we hope to optimize the overall performance of MnAl-C magnets

Methods

Results

Conclusion