Multi-Objective Optimization of aluminum hollow tubes for vehicle crash energy absorption using a genetic algorithm and neural networks
作者: Javad MarzbanradMohammad Reza Ebrahimi
作者单位: 1School of Automotive Engineering, Iran University of Science and Technology, Tehran, Iran
刊名: Thin-Walled Structures, 2011, Vol.49 (12), pp.1605-1615
来源数据库: Elsevier Journal
DOI: 10.1016/j.tws.2011.08.009
关键词: Circular tubeAxial crushingMulti-Objective OptimizationGenetic algorithmArtificial neural network
原始语种摘要: Abstract(#br)A numerical study of the crushing of thin-walled circular aluminum tubes has been carried out to investigate their behaviors under axial impact loading. These kinds of tubes are usually used in automobile and train structures to absorb the impact energy. A Multi-Objective Optimization of circular aluminum tubes undergoing axial compressive loading for vehicle crash energy absorption is performed for five crushing parameters using the weighted summation method. To improve the accuracy of the optimization process, artificial neural networks are used to reproduce the behavior of the crushing parameters in crush dynamics conditions. An explicit finite element method (FEM) is used to model and analyzed the behavior. A series of aluminum cylindrical tubes are simulated under axial...
全文获取路径: Elsevier  (合作)
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影响因子:1.231 (2012)

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关键词翻译
关键词翻译
  • crash 事故
  • vehicle 
  • algorithm 算法
  • aluminum 
  • neural 神经系统的
  • genetic 遗传的
  • crushing 压碎
  • absorption 吸收
  • optimal 最佳的
  • automobile 汽车