Multiobjective Optimization of Composite Square Tube for Crashworthiness Requirements Using Artificial Neural Network and Genetic Algorithm

Abstract

Design optimization of composite structures is of importance in the automotive, aerospace, and energy industry. The majority of optimization methods applied to laminated composites consider linear or simplified nonlinear models. Also, various techniques lack the ability to consider the composite failure criteria. Using artificial neural networks approximates the objective function to make it possible to use other techniques to solve the optimization problem. The present work describes an optimization process used to find the optimum design to meet crashworthiness requirements which includes minimizing peak crushing force and specific energy absorption for a square tube. The design variables include the number of plies, ply angle and ply thickness of the square tube. To obtain an effective approximation an artificial neural network (ANN) is used. Training data for the artificial neural network is obtained by crash analysis of a square tube for various samples using LS DYNA. The sampling plan is created using Latin Hypercube Sampling. The square tube is considered to be impacted by the rigid wall with fixed velocity and rigid body acceleration, force versus displacement curves are plotted to obtain values for crushing force, deceleration, crush length and specific energy absorbed. The optimized values for the square tube to fulfill the crashworthiness requirements are obtained using an artificial neural network combined with Multi-Objective Genetic Algorithms (MOGA). MOGA finds optimum values in the feasible design space. Optimal solutions obtained are presented by the Pareto frontier curve. The optimization is performed with accuracy considering 5% error.