Introducing advanced nanocomposites to enhance the efficiency and stability of the car’s hood door subjected to axial loading via deep neural networks in the mathematical framework

Abstract

In order to improve the efficiency and stability of automobile hood doors under axial loading circumstances, this research investigates the reinforcing of these doors using graphene oxide powders (GOP). This work is separated into two different parts. In the first part, using the mathematical modeling, the results of the presented applicable structure are obtained. After that using the datasets of the mathematical modeling section, the results of deep neural networks (DNN) are trained, tested, and validated. The work focuses on double-curved panels’ linear strain fields, which are an essential part of vehicle construction. We solve the governing equations and boundary conditions for the reinforced automobile hood doors by using Navier’s solutions. GOP greatly enhances the mechanical characteristics by offering better load-carrying capability and deformation resistance. Our results show that DNNs can reliably and effectively forecast the performance of GOP-reinforced structures, providing a useful tool for improving automobile hood door designs. Enhanced automobile safety and durability are made possible by the combination of cutting-edge computational methodologies and novel materials, which also meet the increasing need for high-performance car components. This work highlights the revolutionary power of both technologies on contemporary vehicle design and manufacture, underscoring the mutually beneficial relationship between nanomaterial reinforcement and machine learning in the advancement of automotive engineering.