在分析传统单向拉伸实验的缺点的基础上 ,提出了一种应用神经网络、有限元模拟和实验相结合的板材塑性参数的识别模型。采用了具有双向应力状态的板料拉伸试件 ,利用 BP神经网络实现试件拉伸变形过程中载荷、位移曲线与材料性能参数之间的映射关系 ,从而可以得到双向应力状态和大变形条件下的材料参数。研究中对 0 8Al、LY1 2 M两种材料进行了实验验证 Based on the analysis of the shortcomings of traditional uniaxial tensile test, a model of plastic parameter identification of sheet metal is proposed by using neural network, finite element simulation and experiment. The tensile test specimen with biaxial stress is adopted. By using BP neural network to realize the mapping relationship between the load and displacement curves and the material performance parameters during the tensile deformation, the bi-directional stress state and the large deformation condition Under the material parameters. In the study, two materials, 0 8 Al and LY1 2 M, were experimentally verified