Multi-objective optimization of concave radial forging process parameters based on response surface methodology and genetic algorithm

To enhance the forming quality of the forging and minimize the forging cost in the concave radial forging process, this article examines the influence of process parameters (radial reduction increment h, rotation angle beta, friction coefficient mu) on the forging process through numerical simulation. A multi-objective optimization method is employed to balance the objective functions (strain homogeneity E, forging load F). First, sample points for different combinations of process parameters were obtained using a central composite experimental design. Then, a mathematical model between the process parameters and the objective function was established using the response surface method, which underwent variance analysis and sensitivity analysis. Finally, the optimal process parameter combination was determined based on the NSGA-II algorithm and satisfaction function. The optimization results were verified by finite element simulations. The optimized process combination: increment h = 0.25 mm, beta = 21.68 degrees, mu = 0.05. The corresponding E and F are 0.241367 and 577.029, respectively. Compared with the initial process, the standard deviation of the overall strain was reduced by 14.25%, and the forging load was reduced by 1.76%. The results indicate that the quality of the forgings was significantly improved while the forging cost was reduced to some extent.