Machine-learning Aided Multi-objective Optimization of Electric Machines - Geometric-feasibility and Enhanced Regression Models

This paper deals with the optimization of electrical machines by means of ANN-based classification and regression models. Geometrically (or otherwise) unfeasible designs are detected with high accuracy during the optimization process by means of an ad hoc classification model, whereas continuous targets are predicted through regression models. Training samples are generated with an expensive finite-element (FE) model, resulting in small training sets. Moreover, the design optimization normally involves multiple but correlated sub-objectives. The correlation can be leveraged using chained regression or a multi-output ANN; it is shown that both methods can achieve higher predictive performance than predicting the targets separately. The developed methods are successfully applied to a permanent-magnet synchronous machine (PMSM) with 12 geometric parameters and 4 sub-objectives, and considering both no-load and load operation. The results show very good predictive performance of the ANN models and a significant reduction of the computational effort. The optimization can thus be run several times, with e.g. modified weighting of the sub-objectives, with little extra cost.