Multiobjective optimization of composite cylindrical shells for strength and frequency using genetic algorithm and neural networks

Abstract

In this paper, the optimal fiber orientations relative to the principle axis of composite cylindrical shell composed of four and six layers were determined so that the natural frequency and strength of the shell are optimized. For this purpose, first, the free vibration analysis of the shell was carried out based on 3D elasticity. Then, for calculation of the strength objective function, the inverse form of Tsai-Hill yield criteria was used and the functions of strength and frequency were developed in terms of fiber orientation. Once the correctness of the above solutions was ensured, the objective functions were modeled with artificial neural network (ANN). The model made was then introduced into genetic algorithm (GA) and the maximum fitness function and optimal staking sequence of the layers with respect to the fibers angles were obtained. Optimal solutions obtained by combination of ANN and GA are compared to the solutions obtained by analytical solution and GA; eventually, the tables and diagrams are presented and different fiber orientations as optimization solutions are presented as the final results of the composite shell analysis.

Keywords

• artificial neural network (ann)
• composite cylindrical shell
• frequency
• genetic algorithm (ga)
• optimization
• strength