Free vibration response of auxetic honeycomb sandwich plates using an improved higher-order ES-MITC3 element and artificial neural network

This article presents the combination of mixed interpolation of tensorial components technique of triangular elements and the edge-based smoothed finite element method (ES-MITC3) developed on the higher-order shear deformation theory (HSDT). The free vibration behavior of auxetic honeycomb sandwich plates with the functionally graded material (FGM) skin layers generated by the proposed program is trained and predicted by an artificial neural network (ANN) model using Matlab coding for reducing computational time and saving computer resources, the proposed approach is expected suitable for practical engineering problems. The strain field of edge-based smoothed triangular elements is approximated by using the generalized C-0-HSDT and Lagrange shape functions. Ultra-light features of sandwich plates are obtained by using the auxetic honeycomb core layer (negative Poisson's ratio) and reinforced by FGM skin layers. The present results are compared with other published studies to verify the accuracy and reliability. Besides, the ANN model is also built to train obtained results and accurately predict the natural frequency of structures without running the code. Moreover, the effects of parameters such as geometrical and material properties (especially the auxetic parameters) on the free vibration behavior of auxetic honeycomb sandwich plates with FGM skin layers are investigated in detail.