混合式步进电动机结构参数智能协同优化

为了探究混合式步进电动机关键结构参数对其静态转矩的影响规律,找寻最优设计方案,提出一种基于正交试验、反向传播(Back Propagation,BP)神经网络和遗传算法的智能协同优化方法。通过对电机的磁路分析与ANSYS Maxwell-3D有限元仿真,讨论了电机结构和运行参数对输出转矩的影响,并据此对电机的气隙g、齿宽比b_t/t、齿高h_t和转子壁厚h_r进行协同优化,采用电机的最大静转矩T_m作为优化目标,运用智能协同优化方法得到最优解,并对优化结果进行有限元验证。结果表明:优化结果与仿真结果相近,最大静态转矩相对误差为4.8%,最优结构参数为:g=0.1 mm,b_t/t=0.37,h_t=0.84 mm,h_r=2.5 mm。

Intelligent Collaborative Optimization of Structure Parameters for Hybrid Stepper Motor

In order to explore the influence of the main structure parameters of hybrid stepper motor on its static torque, and find optimal structure size of motor, an intelligent collaborative optimization method based on orthogonal test, BP (Back propagation) neural network and genetic algorithm is proposed. Through the magnetic circuit analysis of the motor and the ANSYS Maxwell-3D finite element simulation, the influence of the motor structure and operating parameters on the output torque is discussed.when the intelligent collaborative optimization method is applied to main Tooth layer structure parameters including the air gap g, tooth width ratio b_t/t, tooth height h_t and rotor wall thickness h_r of the motor, The maximum static torque of the motor can be found.The optimal results are certificated by finite element simulation eventually. The results show that the optimal results are close to the simulation results, the maximum static torque relative error is 4.8%, and the Optimal tooth layer structure parameters are g=0.1 mm, b_t/t=0.37, h_t=0.84 mm, h_r=2.5 mm.