Robust-Oriented Optimization of Flux Switching Machines as Reliable Solutions for Heavy-Duty Machinery Considering Manufacturing Tolerances

This paper presents a robust-oriented optimization methodology tailored for flux-switching wound field machines (FSWFMs), aiming to address the stringent performance and reliability requirements of heavy-duty machinery. Leveraging the Design for Six Sigma (DFSS) framework, the proposed approach effectively mitigates the impact of manufacturing tolerances on critical machine parameters. By incorporating statistical reliability measures and multi-objective optimization, the study achieves notable advancements in machine performance. Specifically, the robust design model demonstrates a 12% enhancement in average torque, reaching 53.3 Nm, while reducing torque ripple by an impressive 70%, lowering it to 4.43%. Comparative analyses between the initial and robust models reveal a significantly narrower performance distribution in the robust design, indicating higher reliability and reduced sensitivity to parameter variations. These outcomes not only validate the efficacy of the DFSS-based optimization but also highlight its potential to revolutionize the design process of electric machines. This methodology bridges the gap between theoretical optimization and practical deployment, offering a scalable and reliable solution for electrification in heavy-duty industrial applications.