Multi-Objective Crashworthiness Optimization of Composite Hat-Shape Energy Absorber Using GMDH-Type Neural Networks and Genetic Algorithms

Reducing the weight of car body and increasing the crashworthiness capability of car body are two important objectives of car design. In this paper, a multi-objective optimization for optimal composite hat-shape energy absorption system is presented At the first, the behaviors of the hat shape under impact, as simplified model of side member of a vehicle body, are studied by the finite element method using commercial software ABAQUS. Two meta-models based on the evolved group method of data handling (GMDH) type neural networks are then achieved for modeling of both the absorbed energy (E) and the Tsai-Hill Failure Criterion (TS) with respect to geometrical design variables using those training and testing data obtained models. The obtained polynomial neural meta-models are finally used in a multi-objective optimum design procedure using NSGA-II with a new diversity preserving mechanism for Pareto based optimization of hat-shape. Two conflicting objectives such as maximizing the energy absorption capability (E), minimizing the Tsai-Hill Failure Criterion are considered in this work.