Geometry optimization of a thin-walled element for an air structure using hybrid system integrating artificial neural network and finite element method

This paper presents the application of an integrated system based on artificial neural networks and calculations by the finite element method (FEM) for the optimization of geometry of a thin-walled element of an air structure. The main criterion of optimization was to reduce the structure's weight w at the lowest possible deformation (high stress level) of the tested object. The objective of the analyses - using artificial neural networks (ANN) - was to investigate the effect of 4 individual variables defining geometry of the model (including: system of ribs and their inclination, system of holes in ribs and side walls) on its deformation and final value of the reduced weight w. Numerical analysis showed that the most important variable is the diameter of holes in the side walls of the model.