Abstract: In this paper，first，a reconfigurable flexible die for the three-dimension stretch bending forming is de-signed，and the springback of profiles during the forming is predicted by means of the support vector regression and the finite element simulation． Then，six factors that greatly affect the springback magnitude (including material pa-rameters，geometrical parameters and process parameters) are analyzed by using the finite element method，and their impact trends on the springback are also investigated． Moreover，these six factors are employed to design a simulation of three-dimension finite-element stretch bending，and the simulated results are used to train and test the springback prediction model based on the support vector regression machine． Finally，for the purpose of verifying the proposed apringback prediction model，the predicted results are compared with those obtained by the widely-used neural network forecasting method and the finite element simulation． It is found that the proposed model is more accurate than the neural network-based method，and that，in experiments，suitable compensations to the die shape according to the springback value predicted by the model may effectively reduce the springback and the shape deviation．

Key words: profile, springback prediction, support vector regression machine, artificial neural networks, three-di-mension stretch bending forming