Multi-Objective Topology Optimization of Synchronous Reluctance Motor with Autoencoder Simultaneously Considering Material Selection and Shape Change

Generally, the optimal shape depends on the characteristics of the magnetic materials used. In this study, we propose an efficient multi-objective topology optimization method that simultaneously considers material selection and shape change. The proposed approach utilizes an Autoencoder, a type of deep learning model, to compress the information of both the device's shape and material characteristics into a latent space. The method enables us to efficiently derive superior combinations of shapes and materials for improving device's performances as pareto solutions. Some numerical examples demonstrate we can successfully carry out the practical device design by combining the proposed approach with the level set method.