The accurate prediction of the spring back has great significance to the cold bending of plates. Based on theanalysis of the square non pressure head CNC bending machine forming principle, the finite element model of the coldforming of the hull plate surface was established using the ANSYS/LS-DYNA finite element software. And the springback computing research was done on the thickness of 8 mm to 16 mm. The influence rule of the thickness to spring backwas analyzed. And the numerical simulation and experimental results of spring back comparison verified the reliability offinite element simulation. Then the prediction model of the plate thickness and the spring back was established using neuralnetwork which is based on nonlinear dynamic system and the test sample spring back was predicted. The results ofsimulation show that the BP neural network can predict the spring back transformation trend very well by comparisonwith the results of numerical simulation and provides a reliable basis for spring back control. A new idea was proposed forthe ship hull plate CNC forming by the application of neural network.
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