Robust Optimization Considering Probabilistic Magnetic Degradation

Abstract

This paper presents robust topology optimization of electromagnetic machines taking account of magnetic degradation caused by mechanical and thermal stresses in punching, shrinking fitting and other manufacturing processes. The topology optimization is performed using two methods; one is the robust genetic algorithm in which random noises are added to the magnetic-characteristic parameters, and another takes the deviations in the objective and constraint functions due to the degradation into account. These methods are applied to optimization of the flux barrier shapes in an IPM motor to find that one can successfully realize robust design.

Effect of Different Cutting Techniques on Magnetic Properties of Grain Oriented Steel Sheets and Axial Flux Machines

“…The effect of cutting was included in the optimal design of the SyRM by Liu and Lee (2018) and high-speed surface mounted PMSM by Martin et al (2018). Hidaka et al (2015) proposed a robust optimisation topology which considers the uncertainties introduced by manufacturing processes in the magnetic properties of steel. These studies confirmed the design changes in the final optimised geometry if the cutting effect is considered in the optimisation routine.…”

Multi-objective topology design optimization combined with robust optimization

“…To solve this problem, two robust optimization techniques were constructed. One is a hybrid robust optimization technique that uses MORDO and a robust genetic algorithm (RGA) (Tsutsui and Ghosh, 1997;Maruyama and Igarashi, 2008;Maruyama et al, 2009;Hidaka et al, 2015) as a hybrid (Yamazaki et al, 2022). The advantage of this method is the reduced computational complexity.…”

Study on sequence problem optimization using variation-robust multi-objective optimization genetic algorithm

“…Moreover, in a design problem requiring the finite element method or a similar, the calculation cost for one evaluation value calculation is high; thus, in a robust optimization method requiring recalculations at multiple sampling points to evaluate one solution candidate, it is difficult to obtain an optimum solution in a practical calculation time. Recently, a robust genetic algorithm (GA) optimization method has been proposed as a method to compensate for this defect (Tsutsui and Ghosh, 1997) (Maruyama and Igarashi, 2008) (Maruyama et al, 2009) (Hidaka et al, 2015). In this method, the GA is used as an optimization engine, and a variation is generated for each generation evolution by the GA.…”