Delamination, frequency, and bending analysis of GPLRC curved panel with initial crack via machine learning and three-dimensional layerwise theory

In the present study, the thermal stability of graphene-reinforced composite laminates (GPL-RC) with diverse functional gradients and width delamination layers is examined. In this regard, various models of laminated GPL-RC are considered with different geometrical and material parameters. Utilizing the physics-informed neural networks (PINN), we calculate the energy release rate (ERR) at the cleavage boundary, aiming to gauge cleavage growth potential. This study also delves effects of various graphene reinforcement distributions and delamination configurations on the vibrational attributes of delaminated GPL-RC sheets, with an emphasis on pre/post heat bending modalities. Solutions are grounded in the third-order shear strain theory (TSDT), integrating von Karman geometric nonlinearity. Using the principle of minimal potential energy, the nonlinear equilibrium equations are tackled using PINN. Theoretical insights obtained are verified via a comparison to other published studies. Notably, parametric experiments indicate that the ERR in the FGX configuration in which most reinforcement material located adjacent to the upper and lower surfaces of the plate, is double that of the FGA, in which most reinforcement material adjacent to the lower surface of the plate. Moreover, while the FGX sheet's fundamental frequency surpasses other graphene configurations at the primary temperature, its natural frequency in the post-buckling modality is notably the least compared to the entire sample set.