Investigation of Optimal Megnetic Properties in NdFeB Magnets by Artificial Neural Network

【Author】

Lian Lixian, Liu Ying ,Hu Wang, Hou Tinghong,Gao Shengji ,Tu Mingjing (College of Materials Science & Engineering, School of Computer Science & Engineering, Sichuan University, Chengdu 610065, China)

【Abstract】

<正> In order to study the effect of alloy component on magnetic properties of NdFeB magnets, the experiment schemes are carried out by the uniform design theory, and the relationship between the component and the magnetic properties is established by artificial neural network( ANN) predicting model. The element contents of alloys are optimized by the ANN model. Meanwhile, the influences of mono-factor or multi-factor interaction on alloy magnetic properties are respectively discussed according to the curves ploted by ANN model. Simulation result shows that the predicted and measured results are in good agreement. The relative error is every low, the error is not more than 1.68% for remanence Br, 1.56% for maximal energy product (BH)m, and 7. 73% for coercivity Hcj. Hcj can be obviously improved and 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 the magnetic properties of NdFeB magnets. The ANN prediction model presents a new approach to investigate the nonlinear relationship between the component and the magnetic properties of NdFeB alloys.

【Keywords】

metal materials; artificial neural network; uniform design; NdFeB; magnetic properties; rare earths

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