  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.
