A machine learning-based model for the estimation of the critical thermo-electrical responses of the sandwich structure with magneto-electro-elastic face sheet

The aim of current work is to evaluate thermo-electrical characteristics of graphene nanoplatelets Reinforced Composite (GNPRC) coupled with magneto-electro-elastic (MEE) face sheet. In this regard, a cylindrical smart nanocomposite made of GNPRC with an external MEE layer is considered. The bonding between the layers are assumed to be perfect. Because of the layer nature of the structure, the material characteristics of the whole structure is regarded as graded. Both mechanical and thermal boundary conditions are applied to this structure. The main objective of this work is to determine critical temperature and critical voltage as a function of thermal condition, support type, GNP weight fraction, and MEE thickness. The governing equation of the multilayer nanocomposites cylindrical shell is derived. The generalized differential quadrature method (GDQM) is employed to numerically solve the differential equations. This method is integrated with Deep Learning Network (DNN) with ADADELTA optimizer to determine the critical conditions of the current sandwich structure. This the first time that effects of several conditions including surrounding temperature, MEE layer thickness, and pattern of the layers of the GNPRC is investigated on two main parameters critical temperature and critical voltage of the nanostructure. Furthermore, Maxwell equation is derived for modeling of the MEE.