Surrogate-Based Multi-Objective Optimization of Electrical Machine Designs Facilitating Tolerance Analysis

Multi-objective optimization algorithms are becoming ever more popular in the field of electrical machine design as they provide engineers with an automated way of efficiently exploring huge design spaces when searching for machines that are simultaneously highly competitive regarding several objectives, such as efficiency, material costs, torque ripple, and others. Apart from exhibiting these good target characteristics, a good design should also be robust, i.e., it should not be very sensitive to slight changes in its design parameters as this would either seriously impact production costs or make the physical machine behave differently than its (optimized) computer simulation model. This paper is focused on describing how global surrogate models (i.e., nonlinear regression models), that are created in order to reduce the dependence on finite-element (FE) simulations during the multi-objective optimization run, can be easily reused to perform very fast local and global tolerance/sensitivity analyses of generated designs. While obtained in a fraction of the time required by the complementary FE-based approach, the surrogate-based sensitivity estimates are able to provide accurate and valuable information regarding the robustness of electrical machine designs. Ultimately, by integrating robustness-related information with Pareto front projections, we aim to provide engineers with much clearer pictures of the specific problem-related tradeoffs discovered by the automated design optimization procedure.