A REAL TIME NEURAL NETWORK BASED FINITE ELEMENT ANALYSIS OF SHELL STRUCTURE

In recent years, the finite element method has been widely used as a powerful tool in the analysis of engineering problems. In the simulation of deformable objects using the finite element method, a complex system of nodes which make a mesh grid is used. The FEM model includes material and structural properties, which altogether determine the model’s response to certain loading conditions. A reliable simulation is supposed to provide for an easier, faster and less expensive development of structures. The real-time simulation of shell-type deformable objects using the finite element method for a non-linear analysis is a challenging task because of the need for fast systems that do not demand high computational cost. In this paper, we present an efficient method based on neural networks for simulating the real-time behavior of a thin walled structure modeled by the finite element method in the commercial FE software. Using the finite element method, the structures displacements are computed offline, by applying forces in the specified range. In the online application mode, a trained neural network is used for obtaining required results for specified loads