Prediction of Hard Magnetic Properties for Melt-Spun Nd2Fe14B and Nd2Fe14B/Fe3B Based on Machine Learning

Abstract

The permanent magnetic properties of Nd-Fe-B magnets strongly depend on the alloy composition. Machine learning is based on mathematical and information science methods and uses existing Nd-Fe-B data to predict the magnetic properties of Nd-Fe-B materials. We use the ensemble learning boosting method to establish the gradient boosting regression tree (GBRT) model for Nd₂Fe₁₄B melt-spun bonded magnets, in comparison with three other methods of machine learning: support vector machine (SVR), multiple linear regression (MLR), and random forest (RFR). The results show that the machine learning GBRT model developed using the ensemble learning algorithm has higher prediction accuracy and better stability than those three traditional machine learning (SVR, MLR, RFR) models used in the past to predict the magnetic properties of melt-spun Nd-Fe-B bonded magnets. We also used the GBRT model to predict hard magnetic properties of melt-spun Nd₂Fe₁₄B/Fe₃B composite materials. Several new alloy compositions of melt-spun Nd-Fe-B bonded magnets and Nd₂Fe₁₄B/Fe₃B composite materials with high-performances were also predicted. Machine learning based on the GBRT model can play an important role in the design, preparation, and development of melt-spun Nd₂Fe₁₄B bonded magnets and Nd₂Fe₁₄B/Fe₃B composite materials.