Generalization Performance in Predicting Torque Characteristics Using Convolutional Neural Network and Stator Magnetic Flux

This study proposes a novel approach using convolutional neural networks (CNNs) to predict the torque characteristics of an interior permanent magnet (IPM) motor. The proposed method allows CNNs to predict the average torque and torque amplitude under various magnet configurations using the magnetic flux density distribution of the stator core. The proposed method, compared with methods trained only on the magnetic flux density distributions of the rotor core, improves the generalization performance in predicting torque characteristics. This significantly reduces the training data. In addition, the topology optimization (TO) time using the proposed method is reduced by 45% compared with the conventional method that only utilizes the finite element method (FEM).