Crashworthiness Design for Cylindrical Tube using Neural Network and Genetic Algorithm

AbstractIn this article, the multi-objective optimization of cylindrical aluminum tubes under axial impact load is presented. Absorbed energy and specific absorbed energy are considered as objective functions while the mean crush load should not exceed allowable limit. The geometric dimensions of tubes including diameter, length and thickness are chosen as design variables. The Non-dominated Sorting Genetic Algorithm –II (NSGAII) is applied to obtain the Pareto optimal solutions. A back-propagation neural network (ANN) 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 using drop hammer