Machine learning assisted nonlinear deflection analysis of agglomerated carbon nanotube core smart sandwich plate with three-phase magneto-electro-elastic skin

Abstract

This research focuses on evaluating the combined effects of the interphase of the three-phase magneto-electro-elastic (TPMEE) composites and carbon nanotubes (CNTs) agglomeration on the nonlinear deflection of the multifunctional sandwich plate, using an artificial neural network (ANN) assisted finite-element (FE) approach. The data points collected from the in-house developed FE computational tool are used to train the ANN model. To this end, a backpropagation-based Levenberg–Marquardt algorithm is used. The core of the plate is made of agglomerated CNTs, and the facesheets are made of TPMEE composites. Two different agglomeration states, partial and complete, are considered for evaluation. Also, three variations of CNTs arrangement are assumed. On the other hand, the interphase effects are incorporated through its volume fractions and compositions. The plate kinematics is based on the higher-order shear deformation theory, and the nonlinearity is assumed to follow von Karman's strain-displacement relation. The equations of motion are derived using the total potential energy principle. Finally, the direct iterative method is used to arrive at the solutions. The numerical examples are also provided to understand the influence of coupling fields associated with the parameters, such as agglomeration, interphase volume fraction, interphase compositions and CNTs arrangement.