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

Abstract

In order to improve the forming quality of the forging and reduce the forging cost in the concave radial forging process. In this paper, the influence of process parameters (radial reduction ∆h, rotation angle β, friction coefficient μ) on the forging process is investigated by numerical simulation, and the trade-off between the objective functions (strain homogeneity 𝐸, forging load 𝐹) is achieved by a multi-objective optimization method. First, sample points for different combinations of process parameters were obtained by the central composite experimental design. Then the mathematical model between the process parameters and the objective function was established using the response surface method, and the model was subjected to variance analysis and sensitivity analysis. Finally, the optimal process parameter combination was obtained according to the NSGA-II algorithm and the satisfaction function. The optimization results were also verified by finite element simulations. The optimized process combination: ∆ℎ = 0.25 𝑚𝑚, 𝛽 = 21.68°, 𝜇 = 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 is reduced by 14.25% and the forging load is 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.

Research on Multi-Objective Process Parameter Optimization Method in Hard Turning Based on an Improved NSGA-II Algorithm

“…With the advancement of manufacturing technologies, intelligent neural networks have increasingly been integrated into the machining of high-hardness materials [1,2] and the optimization of multi-objective process parameters [3][4][5]. As the complexity of workpieces escalates and process requirements become more stringent, optimizing for a single objective is no longer feasible.…”