Application of artificial intelligence in magnetic performance prediction of sintered Nd-Fe-B magnets

This study aims to accurately predict the magnetic properties of sintered NdFeB magnets using artificial intelligence technology. Specifically, a BP neural network was adopted to build a prediction model. In the process of model construction, we specifically considered the complexity of sintered NdFeB magnet compositions and the influence of processing on the residual magnetization (Br) and coercivity (Hcj) magnetic properties. Therefore, we selected element compositions and processing parameters as feature variables. Through optimization of model hyperparameters, we successfully obtained a BP neural network prediction model with high prediction accuracy. The research results demonstrate that the proposed prediction model can effectively predict the magnetic properties of sintered NdFeB magnets, providing important guidance for process optimization and magnet composition design.

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Publication Details

Published
2024-08-04
DOI
https://doi.org/10.1109/icma61710.2024.10633131
Citations
1
Primary Topic
Magnetic Properties of Alloys
Type
article
Field-Weighted Citation Impact
0.12

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article

Application of artificial intelligence in magnetic performance prediction of sintered Nd-Fe-B magnets

Xiaodong Xing, Nan Dong, Shuyong Jiang, Qinglang Zhou
1 citations
Magnetic Properties of Alloys
0.12
article

Application of artificial intelligence in magnetic performance prediction of sintered Nd-Fe-B magnets

Xiaodong Xing, Nan Dong, Shuyong Jiang, Qinglang Zhou
article en
1 citations

Abstract

This study aims to accurately predict the magnetic properties of sintered NdFeB magnets using artificial intelligence technology. Specifically, a BP neural network was adopted to build a prediction model. In the process of model construction, we specifically considered the complexity of sintered NdFeB magnet compositions and the influence of processing on the residual magnetization (Br) and coercivity (Hcj) magnetic properties. Therefore, we selected element compositions and processing parameters as feature variables. Through optimization of model hyperparameters, we successfully obtained a BP neural network prediction model with high prediction accuracy. The research results demonstrate that the proposed prediction model can effectively predict the magnetic properties of sintered NdFeB magnets, providing important guidance for process optimization and magnet composition design.

Harbin Engineering University (CN), Taiyuan University of Technology (CN)
Harbin Engineering University
Openalex Percentile: Top 67%
Magnetic Properties of Alloys
0.12
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