Multi-Objective Yield Optimization for Electrical Machines using Machine Learning

TL;DR

This paper presents a machine learning-based approach for multi-objective optimization of electrical machines, balancing reliability and performance while accounting for manufacturing uncertainties.

Contribution

It introduces a framework combining blackbox machine learning with multi-objective optimization methods for electrical machine design under uncertainty.

Findings

Blackbox ML methods efficiently quantify manufacturing uncertainties.

Comparison of four multi-objective optimization approaches.

Demonstration on a permanent magnet synchronous machine.

Abstract

This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertainty, blackbox machine learning methods are employed. A multi-objective optimization problem is formulated, maximizing simultaneously the reliability, i.e., the yield, and further performance objectives, e.g., the costs. A permanent magnet synchronous machine is modeled and simulated in commercial finite element simulation software. Four approaches for solving the multi-objective optimization problem are described and numerically compared, namely: epsilon-constraint scalarization, weighted sum scalarization, a multi-start weighted sum approach and a genetic algorithm.