Numerical study of the influence of layers number and their orientation in a CFRP using improved ANN based on Enhanced Jaya

Abstract

The advancement of technology in the field of artificial intelligence has facilitated significant progress in various areas, particularly in optimizing the structural configuration of multilayer composites. This study's primary objective is to investigate how geometric parameters, such as fiber orientation and the number of layers influence the mechanical properties of these materials. To predict fracture toughness in bending tests, we employed a hybrid optimization technique (E-Jaya-ANN) and compared it with the Jaya-ANN method to assess the improved approach's accuracy. Furthermore, we developed a numerical model based on the Hashin damage criterion using ABAQUS software to anticipate the response of composite specimens (CFRP) when subjected to a bending load. We validated the reliability of the code results by comparing them with experimental data. Subsequently, we generated a series of numerical results, representing various practical scenarios, to serve as the foundation for training an artificial neural network (ANN). This enabled us to obtain an enhanced architecture for the laminate layers.