Application of Neural Network Prediction in the Optimization of Forming Process of Magnetic Ceramic/Rare Earth Alloy Composite Magnets

Abstract

Abstract In order to ensure that magnet materials have higher mechanical properties and magnetic properties at the same time, magnetic ceramics and rare earth alloys are combined to form composite magnets by bonding and pressing. The nonlinear relationship from input (pressing pressure and holding time) to output (coercivity, density and compressive strength) was established by neural network platform, and the optimization of pressing process was studied. The results show that the density, compressive strength and coercivity of the material increase with increasing pressure. When the pressing pressure exceeds a certain value, the density and compressive strength of the material increase slowly, but the coercivity of the material will decrease. The reason for the decrease of coercivity is related to the crushing of the fast quenched rare earth alloy powders in the form of scales due to excessive pressure and long holding time. The experimental results show that the optimum process parameters with high compressive strength and high coercivity can be found by the prediction of neural network.