基于机器学习的快淬NdFeB磁体永磁性能分析与预测

doi:10.11896/cldb.24030158

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Abstract The permanent magnetic performance of NdFeB magnets strongly depends on the alloy composition and process parameters. Machine lear-ning is based on mathematical and information science methods, using existing rapidly quenched NdFeB magnet data to predict the magnetic properties of NdFeB magnets. In this work, the eXtreme Gradient Boosting (XGBoost) algorithm was used to analyze the permanent magnet performance of rapidly quenched NdFeB magnets. The results show that the machine learning model developed from ensemble learning XGBoost algorithm has a higher prediction accuracy and better stability for the permanent magnet performance of rapidly quenched NdFeB magnets than other algorithms. Then the machine model based on XGBoost algorithm proposed here is utilized to optimize and predict new melt-spun NdFeB mate-rials with high permanent magnetic performance.

Key words： NdFeB magnetic property alloy composition process parameter XGBoost

Published: 25 April 2025 Online: 2025-04-18

CLC number:

TM273

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	Articles by authors
	WEN Jintai
	HU Huaigu
	AN Jiangshan
	HAN Ting
	LI Xinyu
	HU Jifan

Cite this article:

WEN Jintai,HU Huaigu,AN Jiangshan, et al. Machine Learning-based Analysis and Prediction of Hard Magnetic Performance for Melt-spun NdFeB Magnets[J]. Materials Reports, 2025, 39(8): 24030158-7.

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https://www.mater-rep.com/EN/10.11896/cldb.24030158 OR https://www.mater-rep.com/EN/Y2025/V39/I8/24030158

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