Micromechanical Modeming of Hybrid Glass Fiber Laminated Composites Added with Graphene Nano Platelets

Abstract

The standard engineering materials are being fast replaced by composite materials due to their superior strength to weight ratio, ability to be custom-tailored, and their remarkable stiffness characteristics. Due to the above mentioned positive traits, these novel materials are finding their application in every wakes of life and are no more considered as a space-age material. In the last decade, composites with nanofillers infused matrix are a major research topic due to its potential to overcome the inherent weakness of laminated composite materials like weak intralaminar fracture toughness, tendency to delaminate, etc. Among the different types of carbon-based nanofillers, graphene is the most novel one. As such, the requirement for prediction of the properties of graphene-based laminated composites has arisen. In the present study, an attempt has been made to predict the stiffness matrix of the graphene nanoplatelet infused glass fiber reinforced laminated composites using micromechanical methods. Provision for accommodating the agglomeration and property variation of graphene is made in the program for calculating the stiffness. The mathematical modeling confirms that the agglomeration of nanoplatelets drastically reduces the effective properties of GNP infused laminated composite. The proposed model also shows that a variation in the platelet size can cause significant variation in Young’s Modulus and Poisson’s Ratio. The weight fraction’s under consideration in this study, i.e. 0.5% and 1.0% of GNP improves the tensile properties, interlaminar and intralaminar properties of glass fiber laminated composites. The addition of nanoplatelets increases the stiffness of the GNP infused laminates. A reduction in platelet size of graphene can give better results for laminated composites as the filtering effect of the glass fiber will be reduced and a more even distribution of graphene in the epoxy can be expected. The specimen cutting method and machine error might have caused the significant variation in the predicted and experimental results.