Multi-objective optimization on thermomechanical behaviors of temperature-dependent graphene platelet reinforced sandwich plates

This work attempts to optimize Graphene nanoplatelets (GPLs) distribution in the face sheet of sandwich plates to pursue the minimum thermal deflection and transverse shear stresses at interfaces. Thus, an Improved Legendre Higher-order plate Theory combined with Isogeometric Analysis (ILHT-IGA) is, first, proposed to accurately predict thermomechanical behaviors of GPLs-reinforced sandwich plates, which can ensure the reliability of the optimized results. Then, an accelerated multi-objective optimization approach is proposed to optimize thermomechanical behaviors. The trained machine learning algorithm based on ILHT-IGA is employed as a surrogate model to accelerate the optimization process. Finally, X-shaped GPLs distribution can provide the maximum stiffness to resist thermal expansion. However, X-shaped GPLs distribution on face sheets will result in large difference of stiffnesses at adjacent surfaces of face sheets and core layer. Thus, transverse shear stresses at interfaces are obviously increased. To avoid a sudden increase of transverse shear stresses at interfaces, an alternative optimized GPLs distribution has been obtained, where GPLs gradually increase toward the upper and lower surfaces of face sheets and suddenly decrease near the surface of face sheets. Such distributions can effectively enhance the stiffness of sandwich plates to resist thermal expansion behaviors and decrease transverse shear stresses at interfaces.