Multi-objective optimization of grinding process parameters for complicated worm space surface based on the grey wolf optimization algorithm

Grinding is a critical method for enhancing the quality of worm tooth surfaces, and its process optimization has long been a significant research focus; however, existing methods are insufficient in addressing the nonlinearity and complexity inherent in the grinding of complex surfaces. In this study, a three-objective optimization function tailored for grinding complex spiral surfaces is developed and experimentally validated. We have successfully applied the innovative integration of the Multi-objective Grey Wolf Optimization Algorithm (MOGWO) and the optimization function to optimize the grinding process of the Roller Enveloping Worm Reducer (REWR). To account for actual working conditions, we developed constrained models for grinding ratio and machining rigidity and improved the boundary processing method for MOGWO optimization. The enhanced MOGWO demonstrates superior search capabilities during the optimization process, with its optimal solution outperforming traditional optimization algorithms. The optimized grinding process parameters reduce the grinding time by 17.41%, improve the grinding surface quality by 4.46%, and reduce the grinding cost by 1.12% compared with the conventional machining scheme. This provides practical guidance for optimizing the REWR and other complex surface grinding processes.