A. Khalkhali
S. Samareh Mousavi

In order to reduce both the weight of vehicles and the damage of occupants in a crash event simultaneously, it is necessary to perform a multi-objective optimization of the automotive energy absorbing components. In this paper, axial impact crushing behavior of the aluminum foam-filled thin-walled tubes are studied by the finite element method using commercial software ABAQUS. Comparison of the present simulation results with the results of the experiments reported in the previous works indicated the validity of the numerical analyses. A meta-model based on the feed-forward artificial neural networks are then obtained for modeling of both the absorbed energy (E) and the peak crushing force (Fmax) with respect to design variables using those data obtained from the finite element modeling. Using such obtained neural network models, a modified multi-objective GA is used for the Pareto-based optimization of the aluminum foam-filled thinwalled tubes considering three conflicting objectives such as energy absorption, weight of structure, and peak crushing force.

Aluminum foam, Crashworthiness, MLF, Multi-objective optimization, Genetic Algorithm, Pareto.