In stamping process, springback is always determined by process parameters, such as blank-holder force, mould parameters, material parameters, and so on. Prediction of springback and parameters is a multi-objective optimization problem. Firstly, based on the same quantity of orthogonal experimental samples, prediction accuracy and efficiency of back propagation neural network (BPNN) prediction model and the response surface prediction model (RSPM) for springback of S-Rail forming were compared. As a result, RSPM was adopted benefit to less influence by sample scale and higher accuracy. Furthermore, a self-adaptive global optimizing of probability search algorithm, neighborhood cultivation genetic algorithm (NCGA) was proposed to optimize the prediction of process parameters. Then optimized parameters can be obtained quickly. Finally, valid of optimized parameters set, as well as the feasible of the prediction model based on both RSPM and NCGA were confirmed by the finite element analysis (FEA) test of S-Rail springback.
[1]
T. Hiroyasu,et al.
NEIGHBORHOOD CULTIVATION GENETIC ALGORITHM FOR MULTI-OBJECTIVE OPTIMIZATION PROBLEMS
,
2002
.
[2]
Yang Yuying,et al.
Multi-objective optimization of sheet metal forming process using Pareto-based genetic algorithm
,
2008
.
[3]
Tomoyuki Hiroyasu,et al.
NCGA: Neighborhood Cultivation Genetic Algorithm for Multi-Objective Optimization Problems
,
2002,
GECCO Late Breaking Papers.
[4]
R. H. Wagoner,et al.
Analytical springback model for lightweight hexagonal close-packed sheet metal
,
2009
.
[5]
Y. Guo,et al.
Response surface methodology for design of sheet forming parameters to control springback effects
,
2006
.