关键词: 无铆钉自冲铆, 接头, 反向传播神经网络, 预测

Abstract: In order to investigate the property of mechanical clinching joints in the steel-aluminum hybrid structure car body, back-propagation (BP) neural networks were introduced to describe the mapping relationships among such joining technique parameters as sheet thickness, sheet hardness and joint bottom diameter and joints mechanical properties of shearing and peeling. To overcome the existing disadvantages of the standard propagation algorithm in lower training precision, low convergence speed and weak generalization capability, the algorithms of normalization and Levenberg-Marquardt were combined to optimize the standard BP neural network connection weights and improve the model prediction precision and generalization capability. The training and validating samples were performed by the BTM Tog-L-Loc system with different combinations of technique parameters. Then the parameters of training samples and the corresponding joint mechanical properties were supplied to the neural networks for training, and the experimental data validating samples were used to check up the prediction outputs. The results showed that the neural network prediction models after training could effectively predict the mechanical properties of joints and proved to be feasible and reliable, so as to improve the design of the clinching joints applied in steel-aluminum hybrid structure automotive body manufactures.

Key words: mechanical clinching, joint, back-propagation neural network, prediction