Six Sigma-Based Mathematical Optimization Framework for Flux-Switching Machines: A Roadmap for Quality, Performance, and Manufacturing Tolerances

Flux-switching wound field machines (FSWFMs) offer high torque density and independence from rare-earth materials, making them promising candidates for sustainable electric vehicles and industrial applications. However, their adoption is limited by challenges such as high torque ripple, efficiency variations, and sensitivity to manufacturing tolerances. This study presents a Design for Six Sigma (DFSS) optimization framework that integrates sensitivity analysis, response surface modeling (RSM), and multi-objective genetic algorithms to address these challenges. The optimized solution reduces torque ripple by 7.69%, improves torque output, and enhances energy efficiency. By incorporating Six Sigma principles, the framework ensures robust performance under manufacturing variations, bridging the gap between theoretical optimization and practical implementation. This scalable and efficient methodology establishes FSWFMs as viable solutions for industrial applications, revolutionizing electric machine design.