Multi-Objective Topology Optimization of Synchronous Reluctance Motors Using Autoencoder-estimated Flux Barrier Shapes

Abstract

This paper presents a novel topology optimization approach for the design of synchronous reluctance motors based on an autoencoder (AE) combined with the level set (LS) method. As the initial shapes of the LS method, the technique uses the shape generated by the AE, which learns the relationship between the objective function values and the design shapes in the optimization process. The proposed method trains the network parameters such that certain latent variable components represent shape features that are correlated with targeted objective functions. Consequently, shape variations that correspond to changes in multiple objective function values can be independently and continuously visualized. This enables the efficient preparation of new structures that are expected to have high performance. Finally, the AE-generated shapes are used as the initial shapes for LS optimization to derive practical Pareto solutions.