FEM-ANN approach to predict nonlinear pyro-coupled deflection of sandwich plates with agglomerated porous nanocomposite core and piezo-magneto-elastic facings in thermal environment

Abstract

The present work deals with evaluating the nonlinear deflections of the smart sandwich plate with agglomerated Carbon Nanotubes (CNTs) porous core and piezo-magneto-electric (PME) facings, using a novel finite element method (FEM) – artificial neural network (ANN) approach. For the first time, an ANN-based computational tool that integrates the effects of agglomeration of CNTs, porosity and pyro-coupling of the PME materials is presented. Firstly, an in-house finite element (FE) computational tool is proposed and developed using the principle of virtual work in association with higher-order shear deformation theory (HSDT) and von-Karman’s nonlinearity. The data points owing to the nonlinear deflections are collected using the proposed FE formulation, which trains the ANN model using Levenberg–Marquardt algorithm. The externally applied thermal loads are assumed to vary uniformly and linearly across the thickness of the plate. The primary focus of this work is to assess the variation in the degree of pyro-coupling associated with agglomeration and porosity. Two states of agglomeration, such as partial and complete; three forms of porosity, such as uniformly distributed, and two variants of functionally graded porosity, are considered for investigation. Numerical examples are solved to understand the interrelated effects of these material properties. A significant variation in the deflection of the plate, which refers to its actuation capability, is witnessed when the parameters of agglomeration and porosity change.