Improved gray wolf optimization algorithm for optimizing ultrasonic rolling parameters of GCr15 steel

Ultrasonic rolling, a synergistic surface-strengthening technique combining high-frequency vibrations with rolling deformation, offers significant potential for enhancing material surface integrity. Conventional optimization approaches, however, often fail to account for the complex interactions among multiple process parameters and competing surface quality indicators. A novel multi-objective optimization framework is developed for ultrasonic rolling of GCr15 steel, integrating second-order response surface methodology with an Improved Gray Wolf Optimization (IGWO) algorithm. Unlike standard metaheuristics, the proposed IGWO enhances global search through Latin hypercube sampling, improves search efficiency, and prevents premature convergence through synchronized cosine–sine convergence modulation and incorporates an adaptive weight strategy to improve local refinement and solution stability. This approach identifies optimal process parameters for surface roughness, residual compressive stress, and microhardness, and these parameters are experimentally validated. The results demonstrate that IGWO-optimized ultrasonic rolling parameters provide a superior balance among all surface quality indicators, effectively reducing surface roughness while significantly improving residual compressive stress and microhardness. The framework addresses the limitations of conventional optimization methods and provides a generalizable, practical strategy for precision surface engineering in high-performance components.