Optimizing the failure analysis of a network structure using artificial neural network and genetic algorithm

In this study, a 4×4 square network structure made of titanium has been optimized under tensile force using relevant parameters. The network structure is a quadrilateral structure with a side length of L and ϴ, and the fracture order of the walls has been compared using MATLAB software and simulation with Abaqus software, and the results of the fracture order of the structure match each other. In the present study, the objective function in optimization is to increase energy absorption and minimize the maximum stress, and the effect of parameters such as side lengths and various angles in this structure has been investigated. 100 different cases have been obtained for values of L and ϴ with output of area under the curve (energy absorbed) and maximum stress and strain using MATLAB software. With input data (L and ϴ) and output data (energy absorbed and maximum stress), a neural network has been trained and a regression model has been used in the neural network to achieve a prediction accuracy of over 99%, which is a high level of accuracy. The relationship function between input and output of the neural network has been obtained using MATLAB software, and the optimization of this 4×4 network structure has been carried out using the genetic algorithm. The objective function in this study is to increase energy absorption and minimize the maximum stress so that the network structure has the highest strength considering the examined parameters.