Abstract:

：Aiming at the twist springback appearing after the stamping process of high-strength steel sheet, the stamping and springback processes are numerically simulated based on the finite element analysis software DYNAFORM. Then a new method to evaluate the torsion springback is proposed, and the design of experiments and the wavelet neural network agent model are used to the optimization research of the twist springback. The flex-rail model is taken into account, taking the value of twist springback as forming target, and the process parameters selected through the orthogonal experiment design as influencing factors. The Latin hypercube is used to sample influencing factors, and the simulation is carried out to get the samples. The wavelet neural network agent model of influencing factors and forming target is built. Then the optimal solution is got by the iteration of improved particle swarm optimization algorithm. The results show that the optimized process parameters can effectively reduce the twist springback, and the research provides a useful method to reduce the twist springback of complex parts.

Key words: parameter optimization, particle swarm algorithm, twist springback, wavelet neural network, high-strength steel