Mechanical Properties Prediction of the Mechanical Clinching Joints Based on Genetic Algorithm and BP Neural Network

For optimal design of mechanical clinching steel-aluminum joints, the back propagation(BP) neural network is used to research the mapping relationship between joining technique parameters including sheet thickness, sheet hardness, joint bottom diameter etc., and mechanical properties of shearing and peeling in order to investigate joining technology between various material plates in the steel-aluminum hybrid structure car body.Genetic algorithm(GA) is adopted to optimize the back-propagation neural network connection weights.The training and validating samples are made by the BTM? Tog-L-Loc system with different technologic parameters.The training samples' parameters and the corresponding joints' mechanical properties are supplied to the artificial neural network(ANN) for training.The validating samples' experimental data is used for checking up the prediction outputs.The calculation results show that GA can improve the model's prediction precision and generalization ability of BP neural network.The comparative analysis between the experimental data and the prediction outputs shows that ANN prediction models after training can effectively predict the mechanical properties of mechanical clinching joints and prove the feasibility and reliability of the intelligent neural networks system when used in the mechanical properties prediction of mechanical clinching joints.The prediction results can be used for a reference in the design of mechanical clinching steel-aluminum joints.

基于改进BP神经网络铆接接头力学性能预测

针对钢铝材料的铆接接头优化设计和结构车身混合材料接头技术问题,使用改进的BP神经网络模型研究钣金材料厚度、硬度、接头底部直径等的接头技术参数与材料自身剪切力与剥离力强度等力学参数的映射关系。ALM算法被用来优化改进的BP神经网络预测模型连接权值,提高了神经网络模型的预测精度和泛化能力。对改进的神经网络的预测结果进行检验的结果表明,训练后的神经网络模型能够准确有效地预测铆接接头力学性能,证实了改进神经网络应用于铆接接头力学性能预测的可行性与可靠性。

基于神经网络的焊接接头力学性能预测平台

基于神经网络建立焊接接头力学性能预测平台,可实现合金钢、钛合金及铝合金焊接接头力学性能预测。预测内容包括抗拉强度、屈服强度、伸长率以及断面收缩率等。同时可以分析参数变化对接头力学性能的影响。算法核心主要是应用遗传算法优化Back-Propagation神经网络连接权,其具有很好的全局搜索特性以及不易陷入局部最优化,同时应用既有高斯-牛顿法的快速收敛特性,也有梯度下降法的局部搜索特性的LM算法,使预测结果具有很好的泛化性能和较高的预测精度。

风电轴承套圈超声滚挤压表层物理力学性能预测模型

为了实现对风电轴承套圈超声滚挤压表层物理力学性能的合理控制,以静压力、进给速度、转速和振幅为加工参数进行超声滚挤压正交试验设计,采用遗传算法对BP神经网络算法进行改进,借助正交试验数据建立风电轴承套圈超声滚挤压表层物理力学性能预测模型,并验证预测模型的准确性,再应用BP神经网络预测模型分析各加工参数对风电轴承套圈表层物理力学性能的影响规律。结果表明:风电轴承套圈表层残余压应力试验值与预测值平均相对误差为3.9%;表面加工硬化程度平均相对误差3.31%,试验值与预测值相差较小,证明了BP神经网络预测模型的预测精度较高;预测结果表明随着静压力的增大,残余压应力和表面硬度呈增大趋势;随进给速度增大,残余压应力和表面硬度减小;转速增大使表面硬度增大,残余压应力减小;随着振幅的增加,残余压应力呈增大趋势,表面硬度呈先增大后减小的趋势。预测模型的预测规律与试验规律相一致,能够实现对风电轴承套圈超声滚挤压表层物理力学性能的预测。