Abstract
The inset-surface permanent magnet (ISPM) machine can achieve the desired electromagnetic performance according to the traditional deterministic design. However, the reliability and quality of the machine may be affected by the essential manufacturing tolerances and unavoidable noise factors in mass production. To address this weakness, a comprehensive multi-objective optimization design method is proposed, in which robust optimization is performed after the deterministic design. The response surface method is first adopted to establish the optimization objective equation. Afterward, the sample points are obtained via Monte Carlo simulation considering the design-variable uncertainty. The Design for Six Sigma approach is adopted to ensure the robustness of the design model. Furthermore, the barebones multi-objective particle swarm optimization algorithm is used to obtain a compromise solution. A prototype is manufactured to evaluate the effectiveness of the proposed method. According to the finite-element analysis and experimental tests, the electromagnetic performance and reliability of the machine are significantly enhanced with the proposed method.
Highlights
The inset-surface permanent magnet (ISPM) machine can achieve the desired electromagnetic performance according to the traditional deterministic design
The electromagnetic performance can be significantly improved by appropriate optimization algorithms, the noise factors, such as uncontrollable manufacturing tolerances of design variables, deviations in material characteristics, and imperfections in assembly, are inevitable, resulting in perturbations to the electromagnetic performance and affecting the reliability and manufacturing cost of the machine [19]
This paper proposes a comprehensive multi-objective optimization method that can efficiently determine the appropriate values of the design variables and consider the unavoidable noise factors, which can significantly reduce the product defect rate during mass production
Summary
Inset-surface permanent magnet (ISPM) machines have been successfully used in various applications, such as electric vehicles, wind power generation, and shift propulsion [1,2,3,4], due to their high efficiency, high torque density, and good weakening capability [5,6,7]. The torque performance can be enhanced, the number of optimization objectives is limited, and the combinations of design variables are restricted. This method can only be used to solve the. DFSS is employed to establish a robust optimization equation considering the manufacturing tolerances, which can satisfy the shortand long-term design requirements [26]. This paper proposes a comprehensive multi-objective optimization method that can efficiently determine the appropriate values of the design variables and consider the unavoidable noise factors, which can significantly reduce the product defect rate during mass production.
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