Using of neural network and genetic algorithm in multiobjective optimization of collapsible energy absorbers

In this article,the multiobjective optimization of cylindrical aluminum tubes under axial impact load is presented. Absorbed energy and specific absorb energy are considered as objective functions while the mean crush load should not exceed allowable limit.The geometric dimentions of tubes including diameter and length of tube are selected as the design variables.The Non-dominated sorting Genetic algorithm –II (NSGAII) is applied to obtain the Pareto optimal solutions.A back-propagation neural networks(ANNs) is constructed as the surrogate model to formulate the mapping between the variables and the objectives.The finite element software ABAQUS/Explicit is used to generate the training and test sets for the ANNs.Validating the results of finite element model,several impact tests are carried out with drop hammer.