Multi-Objective Topology Optimization of Synchronous Reluctance Motor Using Response Surface Approximation Derived by Deep Learning

In this paper, we propose a novel multi-objective topology optimization with response surface approximation interpolating various topologies, which is derived by deep learning with training data in the actual design space. The response surface is constructed by means of autoencoder with the training data of geometrically diverse shapes of targeted devices, which can express the interpolations that combine the multiple characteristic structures. The global topology search is performed based on the gradient information of the response surface, by which we can obtain the initial shapes for level set optimization to efficiently obtain easily manufactured and excellent Pareto optimal solutions. The effectiveness of the proposed method is demonstrated by optimizing multi-flux barriers in a synchronous reluctance motor for the objective functions of average torque and torque ripple.