To control the precision forging product forming quality, a multi-objective optimization method for process parameters design was proposed by applying Latin hypercube design method and response surface model approach. Meanwhile, the deformation homogeneity and material damage of forging product was first presented for evaluating the forming quality. Then, as a case of study, the radial precision forging for a hollow shaft with variable cross section and wall thickness was carried out. The 3D rigid-plastic finite element (FE) model of the radial precision forging was established. The parameters on the forming quality evaluation function study were discussed to investigate the multi-objective optimization model. Non-dominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the Pareto-optimal solutions. A compromise solution was selected from the Pareto solutions by using the mapping method. Experiments with the same parameter settings were compared with the simulations. After conducting radial forging and mechanical property experimental study on the forging product by multi-objective optimization process parameters, the feasibility of the multiobjective optimization method for the precision forging product forming quality was verified.

In order to control the precision forging forming quality and improve the service life of die, a multiobjective optimization method for process parameters design was presented by applying Latin hypercube design method and response surface model approach. Meanwhile the deformation homogeneity and material damage of forging parts were proposed for evaluating the forming quality. The forming load of die was proposed for evaluating the service life of die. Then as a case of study, the radial precision forging for a hollow shaft with variable cross section and wall thickness was carried out. The 3D rigid-plastic finite element (FE) model of the hollow shaft radial precision forging was established. The multiobjective optimization forecast model was established by adopting finite element results and response surface methodology. Nondominated sorting genetic algorithm-II (NSGA-II) was adopted to obtain the Pareto-optimal solutions. A compromise solution was selected from the Pareto solutions by using the mapping method. In the finite element study on the forming quality of forging parts and the service life of dies by multiobjective optimization process parameters, the feasibility of the multiobjective optimization method presented by this work was verified.

The mechanical properties of the steering column have a significant influence on the comfort and stability of a vehicle. In order for the mechanical properties to be improved, the rotary swaging process of the steering column is studied in this article. The process parameters, including axial feed rate, hammerhead speed, and hammerhead radial reduction, are systematically analyzed and optimized based on a multi-objective optimization design. The response surface methodology and the genetic algorithm are employed for optimal process parameters to be obtained. The maximum damage value, the maximum forming load, and the equivalent strain difference obtained with the optimal process parameters are, respectively, decreased by 30.09%, 7.44%, and 57.29% compared to the initial results. The comparative results present that the quality of the steering column is improved. The torque experiments and fatigue experiments are conducted with the optimal steering column. The maximum torque is measured to be 260 NM, and the service life is measured to be 2 weeks (40 NM, 2500 times), which are, respectively, increased by 8.3% and 8.69% compared to the initial results. The above results display that the mechanical properties of the steering column are optimized to verify the feasibility of the multi-objective optimization method.