Topology Optimization for Motor Using Multitask Convolutional Neural Network Under Multiple Current Conditions

This article proposes a new topology optimization (TO) method for a motor that leads to an optimized solution in less time under multiple current conditions while considering the torque ripple-order component. To consider the different characteristics, a multitask CNN is newly employed. The multitask CNN predicts multiple torque performances based on the current conditions and cross-sectional magnetic flux density distribution and applies it to the TO process. As a result, the average torque and harmonic components of the target motor improved by 8.9% and 48.2%, respectively, under multiple current conditions. Furthermore, the computational cost for TO was reduced by 95.1% using the proposed method, compared with that of conventional methods. Therefore, the proposed method enables fast optimization of torque ripple-order components under a wide range of current conditions.