Application of computer approach integrating AI for nonlinear post-buckling behavior of Mindlin cut-out composite plates reinforced with FG-carbon nanotubes

Abstract

Using computer numerical methods to combine certain strengths of different techniques produces enhanced accuracy, efficiency and stability in nonlinear post-buckling analysis. A combination of advanced computer numerical methods is carried out for nonlinear post-buckling computational analysis of Mindin composite plates cut-out by the functionally graded-carbon nanotubes. It combines the energy methodology, Rayleigh–Ritz numerical and domain computer decomposition methods with the Newton–Raphson iterative scheme in order to efficiently solve the governing nonlinear equations. The structural behavior is modeled using first-order shear deformation theory and as the reinforcement variation through the nanocomposite plate thickness is considered, the material property variation from the Mori–Tanaka homogenization technique is used. The effects of several key parameters such as such as CNT distribution, elastic foundation, and external magnetic field on the computational framework are analyzed based on applied computer methods. Based on results, the presence of CNTs enhances the post-buckling performance. An important contribution of this study is robustness of hybrid numerical techniques for prediction of nonlinear post-buckling in CNT-reinforced composite structures.