Elastic surrogate modeling of graphene nanoplatelet-reinforced epoxy using computational homogenization

Abstract

2D nanoparticles, such as graphene or graphite nanoplatelets, are used as additives in polymer matrices to improve their stiffness and electrical conductivity. In this paper, a finite element-based model for homogenized macrolevel stiffness is developed to understand the increase in stiffness of the epoxy matrix induced by graphene nanoplatelets. The model uses image segmentation of regular SEM micrographs to account for the morphology of the graphene platelet network. Here, it is essential to include a fluctuation field in computational homogenization to describe microstructural relaxation. Platelets of the microstructure are modeled as embedded membranes, assuming perfect bonding to the polymer. To estimate the stiffness of the membrane, we used molecular dynamics simulations from a related paper on layered graphene platelets. A novel feature is the identified anisotropic and isotropic elastic surrogate models obtained by least-squares fits of homogenized microstructural responses. Surrogate models serve as a basis for the evaluation of the stiffness of the nanocomposites, and these models are validated through the Halpin–Tsai and Mori–Tanaka models. According to the experimental investigation, the results show that the samples exhibit an increase in stiffness of up to 10 % to 30 % for GNP contents ranging from 1 to 5 wt. %, respectively, obtained from the morphological properties and the weight fraction of the carbon filler.