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mechanical Properties Prediction of the mechanical clinching Joints Based on Genetic Algorithm and BP Neural Network

引用

Chinese Journal of mechanical Engineering 2009年第1期22卷 36-41页

作者： LONG Jiangqi LAN Fengchong CHEN Jiqing YU Ping School of mechanical ＆ Automotive Engineering South China University of Technology Guangzhou 510641 China

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.

关键词： genetic algorithm BP neural network mechanical clinching joint properties prediction

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Optimization of clinching Tools by Integrated Finite Element Model and Genetic Algorithm Approach

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Journal of Shanghai Jiaotong university(Science) 2019年第2期24卷 262-272页

作者： WANG Menghan XIAO Guiqian WANG Jinqiang LI Zhi College of Materials Science and Engineering Chongqing University

clinching is a convenient and efficient cold forming process that can join two sheets without any additional part. This study establishes an intelligent system for optimizing the clinched joint. Firstly, a mathematical model which introduces the ductile damage constraint to prevent cracking during clinching process is ***, an optimization methodology and its corresponding computer program are developed by integrated finite element model(FEM) and genetic algorithm(GA) approach. Secondly, Al6061-T4 alloy sheets with a thickness of 1.4 mm are used to verify this optimization system. The optimization program automatically acquires the largest axial strength which is approximately equal to 872 N. Finally, sensitivity analysis is implemented, in which the influence of geometrical parameters of clinching tools on final joint strength is analyzed. The sensitivity analysis indicates the main parameters to influence joint strength, which is essential from an industrial point of view.

关键词： mechanical clinching optimization design genetic algorithm(GA) ductile damage

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