Multi-Objective Optimization of aluminum hollow tubes for vehicle crash energy absorption using a genetic algorithm and neural networks
 作者： Javad Marzbanrad,  Mohammad 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 tube;  Axial crushing;  Multi-Objective Optimization;  Genetic algorithm;  Artificial 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... impact condition for the experimental validation of the numerical solutions. A finite element code, capable of evaluating parameters crush, is prepared of which the outputs are used for training and testing the developed neural networks. In order to find the optimal solution, a genetic algorithm is implemented. With the purpose of illustrating optimum dimensional ratios, numerical results are presented for thin-walled circular aluminum AA6060-T5 and AA6060-T4 tubes. Multi-Objective Optimization of circular aluminum tubes has been performed in the basis of different priorities to create the ability for designer to select the optimum dimension ratio. Also, crush parameters of two aluminum alloys has been compared.

• 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