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Multi-Objective Yield Optimization for Electrical Machines Using Gaussian Processes to Learn Faulty Design | IEEE Journals & Magazine | IEEE Xplore

Multi-Objective Yield Optimization for Electrical Machines Using Gaussian Processes to Learn Faulty Design


Abstract:

This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertain...Show More

Abstract:

This work deals with the design optimization of electrical machines under the consideration of manufacturing uncertainties. In order to efficiently quantify the uncertainty, a hybrid Gauss-Process regression (GPR) model is employed. In contrast to classic Kriging or Bayesian optimization approaches, we train a GPR surrogate for the performance feature specifications, not for the objective function. 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: \varepsilon-constraint scalarization, weighted sum scalarization, a multi-start weighted sum approach and a genetic algorithm. We show that the efficiency gain thanks to our hybrid GPR model enables even computationally heavy multi-objective optimization for real-world applications.
Published in: IEEE Transactions on Industry Applications ( Volume: 59, Issue: 2, March-April 2023)
Page(s): 1340 - 1350
Date of Publication: 03 October 2022

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