Multiscale modelling on material properties and mechanical behaviours of graphene reinforced polymer nanocomposites

Abstract

Graphene possesses many superior properties, such as ultrahigh mechanical stiffness and strength, exceptional thermal and electrical conductivities as well as excellent optical properties. In many of the envisioned applications, graphene or its derivatives are incorporated into the polymer matrix to form graphene based nanocomposite systems in which the polymer matrices can work synergically with graphene fillers as functional components providing supports and protections to the embedded graphene. Two types of additive manufacturing (AM) techniques have been developed for the graphene reinforced polymer nanocomposites. One is the layer-by-layer (LbL) assembly technique which is a versatile process and capable of manipulating material composition and architectures at the nanoscale. The other AM technique is conventionally known as the extrusion-based 3D printing. This research focuses on the computational method and numerical modelling of material properties and mechanical behaviours of graphene-based polymeric nanocomposites. A hierarchical multiscale analysis approach is adopted and tailored specifically for the graphene-based polymeric nanocomposites fabricated using the AM techniques. Some of the important material characteristics at nano- and meso-scales such as molecular interactions and microstructure morphologies are simulated and discussed in details. The nonlinear mechanical behaviours e.g., bending, post-buckling and vibration of functionally graded graphene reinforced nanocomposite (FG-GRC) beams fabricated by LbL technique are subsequently carried out. Numerical analysis with various macroscaled parameters such as functionally graded patterns, temperature rises as well as foundation stiffnesses are presented and discussed. This study is crucial for engineering applications to evaluate mechanical behaviours of such nanocomposite materials with optimal arrangements and manufactured by using these two above-mentioned methods.

Date of Award	2018
Original language	English