分析了金属板料激光冲击成形的加工过程,为解决不同实验参数下金属板料变形量难以控制,实验参数难以优化的问题,提出了基于神经网络的控制板料变形量的方法,建立了激光加工参数与板料最大变形量之间的神经网络模型,编写了相应的控制软件,并应用该模型对SUS304,LD31,TA2和Al-Mg 4种不同板料在不同实验条件下进行冲击实验。结果表明,采用该方法可有效地优化冲击实验参数,控制板料变形量。 In order to solve the problem that the deformation of sheet metal under different experimental parameters is difficult to control and the experimental parameters are difficult to optimize, a method of controlling the deformation of sheet metal based on neural network is proposed. The laser Processing parameters and the maximum deformation of the sheet metal between the neural network model, the preparation of the corresponding control software, and the application of the SUS304, LD31, TA2 and Al-Mg 4 different plates under different experimental conditions for impact experiments. The results show that this method can effectively optimize the impact of experimental parameters, control the amount of sheet metal deformation.