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,...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.展开更多
首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头...首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头颈部厚度Nt和镶嵌量Ut为重要的中间变量。强度预测公式表明:对于颈部断裂的压印接头,颈部厚度值tN越大,接头强度越高;对于拉脱失效的压印接头,接头强度取决于颈部厚度tN和镶嵌量tU,两者之和越大,接头强度越高,并且镶嵌量对接头强度的影响与颈部厚度相比更大。对颈部厚度变化范围为0.35mm^0.56mm、镶嵌量变化范围为0.045mm^0.45mm的15种组合接头,根据强度预测公式计算了接头强度,并进行了拉伸-剪切试验。将计算结果与试验结果进行对比,结果表明二者吻合较好,最大接头强度误差为8.9%。这说明本文建立的接头强度预测公式能够准确地预测压印接头拉伸-剪切过程的强度和破坏形式。展开更多
分别采用压印连接和压印-点焊复合连接方式对铝锂合金AL1420进行连接,通过对拉伸-剪切试验后失效接头和得到的载荷-位移曲线进行观察和分析,对各组试件进行失效形式和力学性能的对比研究,并对不同焊接电流对接头产生的影响进行探讨。结...分别采用压印连接和压印-点焊复合连接方式对铝锂合金AL1420进行连接,通过对拉伸-剪切试验后失效接头和得到的载荷-位移曲线进行观察和分析,对各组试件进行失效形式和力学性能的对比研究,并对不同焊接电流对接头产生的影响进行探讨。结果表明:焊接电流能有效改变压印接头性能。压印-点焊复合连接较压印连接接头断口处截面变形严重,但接头力学性能优于压印连接接头性能,并且焊接电流每增大1 k A,接头承载力以1. 05倍速率增加;压印-点焊复合连接接头较压印连接接头具有更好的能量吸收特性,并且随焊接电流的增大,能量吸收性能提高。展开更多
基金supported by Guangdong Provincial Technology Planning of China (Grant No. 2007B010400052)State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body of China (Grant No. 30715006)Guangdong Provincial Key Laboratory of Automotive Engineering, China (Grant No. 2007A03012)
文摘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.
文摘首次提出了用于汽车生产中分瓣模压印连接接头强度和失效形式的预测方法。根据接头静力学测试中的颈部断裂失效和上下板拉脱失效两种失效形式分别建立了压印接头的两个强度预测公式,2pπ2N N NF A R t t()和2p pπt b s F R,公式以接头颈部厚度Nt和镶嵌量Ut为重要的中间变量。强度预测公式表明:对于颈部断裂的压印接头,颈部厚度值tN越大,接头强度越高;对于拉脱失效的压印接头,接头强度取决于颈部厚度tN和镶嵌量tU,两者之和越大,接头强度越高,并且镶嵌量对接头强度的影响与颈部厚度相比更大。对颈部厚度变化范围为0.35mm^0.56mm、镶嵌量变化范围为0.045mm^0.45mm的15种组合接头,根据强度预测公式计算了接头强度,并进行了拉伸-剪切试验。将计算结果与试验结果进行对比,结果表明二者吻合较好,最大接头强度误差为8.9%。这说明本文建立的接头强度预测公式能够准确地预测压印接头拉伸-剪切过程的强度和破坏形式。
文摘分别采用压印连接和压印-点焊复合连接方式对铝锂合金AL1420进行连接,通过对拉伸-剪切试验后失效接头和得到的载荷-位移曲线进行观察和分析,对各组试件进行失效形式和力学性能的对比研究,并对不同焊接电流对接头产生的影响进行探讨。结果表明:焊接电流能有效改变压印接头性能。压印-点焊复合连接较压印连接接头断口处截面变形严重,但接头力学性能优于压印连接接头性能,并且焊接电流每增大1 k A,接头承载力以1. 05倍速率增加;压印-点焊复合连接接头较压印连接接头具有更好的能量吸收特性,并且随焊接电流的增大,能量吸收性能提高。
