Topology Optimization With Shapley Additive Explanations for Permanent Magnet Synchronous Motors

This study proposes a novel methodology for topology optimization, employing an explainable deep neural network (XDNN). This innovative approach utilizes Shapley additive explanations (SHAPs), a tool designed to elucidate the predictive reasoning of convolutional neural networks (CNNs). The contributing regions to the characteristics are revealed by SHAP and topology optimization is performed in restricted regions to reduce the computational cost during the search while ensuring an efficient search. As a practical demonstration of the efficacy of the proposed method, it is applied to an interior permanent magnet synchronous motor. The results comprehensively demonstrate the effectiveness and potential implications of the novel methodology.