Transactions of Materials and Heat Treatment

Title：Parameters optimization of stamping and springback for aluminum-alloy sheet based on wavelet neural network and particle swarm optimization algorithm

Authors： Sun Xinqiang Xie Yanmin Tian Yin He Yujun

Unit： Southwest Jiaotong University

KeyWords： aluminum-alloy springback wavelet neural network particle swarm algorithm

ClassificationCode：

year,vol(issue):pagenumber：2015,40(1):137-142

Abstract：

For the large springback appeared after the stamping process of aluminum-alloy sheet, and for reducing the time of optimizing process parameters, the stamping process and springback were numerically simulated based on the finite element analysis software DYNAFORM. On the basis of the numerical simulation consisting with experimental results, the agent model was used to the optimization research of springback. The S-rail of NUMISHEET′96 was taken into account, with the fillet radius of punch, the fillet radius of die, the blank holder force and the sheet thickness as influencing factors, and the maximum springback value after stamping was regarded as forming target. By using latin hypercube to sample, and the simulation was carried to get the samples, and the wavelet neural network agent model between influencing factors and forming target was built. Then the optimal solution was obtained by iterations of particle swarm optimization algorithm. The results show that the wavelet neural network agent model can describe the input-output relationship between the sheet forming process parameters and the springback, and the springback can be remarkably reduced after the optimization.

Funds：

国家自然科学基金资助项目（51005193）

孙新强（1991-），男，硕士研究生

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