用人工神经网络研究合金元素对NdFeB永磁体磁性能的影响

Study for effect of alloying element on megnetic properties of NdFeB magnets by artificial neural network

为了系统研究合金元素对Nd-Fe-Co-Zr-B系永磁合金磁性能的影响,采用均匀设计方法设计了Nd、Co、Zr和B的4因素6水平U18(64)试验方案,根据试验结果,建立了合金成分与磁性能之间的人工神经网络(ANN)预测模型。利用该预测模型获得的成分-性能的二维曲线、三维曲面及等高线图,研究了单个合金元素以及多元素间的交互作用对NdFeB磁体磁性能的影响规律。结果表明:预测结果与实测结果吻合良好,预测精度高;Nd、Zr为提高矫顽力Hcj而降低剩磁Br的元素;Co、B则对提高Br有利而对提高Hcj不利;合金元素对Hcj与Br的影响呈相反的趋势;元素间交互作用对磁性能影响显著。

In order to study the effect of alloying element on megnetic properties of NdFeB magnets, the 4-factors and 6-levels U18 （6^4） experiments were carried out by the uniform design theory, and the relationship between component and NdFeB alloys magnetic properties was established by artificial neural network （ANN） predicting model. 2-dimension curves, 3-dimension figures and contour lines of content-properties were obtained by the ANN model. The influences of single element or the interaction among elements on magnets magnetic properties are respectively discussed according to the curves ploted by ANN model. Simulation result shows that predicted and measured results are in good agreement, prediction precise was high; the coercivity Hcj can be obviously improved and the remanence Br can be reduced by increasing Nd or Zr content; Co and B have advantageous effects on increasing Br and disadvantageous effects on increasing Hcj; influence of alloying elements on Hcj and Br are inverse; and the interaction among the alloying elements play an important role in megnetic properties of NdFeB magnets. The ANN prediction model was a new effective approach to investigate study the effect of alloying element on megnetic properties of NdFeB magnets.