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  Modelling and characterisation of chip formation and machining induced defects of nano-particle toughened Carbon Fibre Polymer Composites


   Department of Mechanical Engineering

  ,  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

Fibre reinforced composites with thermoset polymer matrix are replacing metallic engineering alloys due to their strength/weight ratio specially in the aerospace and automotive industries. These materials are highly brittle, restricting the structural applications and processing of the final products that requires secondary operations such as milling and drilling. Fibre composites can be toughened to overcome the brittleness for structural applications and prevent the subsequent manufacturing induced defects. Various modifiers have been used to improve the toughness of brittle thermoset polymers, including rubbers, ceramic, carbon nanotubes as well as silica nanoparticles. Among these modifiers, silica and rubber nanoparticles have recently attracted a great deal of interests because they do not significantly increase the resin viscosity, making it possible to manufacture fibre composites using the infusion processes. Simultaneously, combining two different kinds of particles, the soft micron-sized particles and the hard nanometre-sized particles in the same formulation, has been shown to have a synergistic effect in increasing the toughness of composites in some cases. This research is following the recently started research program on understanding the effect of nano particles on the mechanical strength and machinability of the nano-particle reinforced CFRP and aims to understand and predict the effect of secondary operations such as drilling and milling on the functional performance of the materials. A modelling platform will be developed in this project where the effect of various compositions of additives on CFRP properties will be simulated using numerical methods the results of which would be used to enrich the available experimental data in order to develop a Design for Manufacture and Performance concept based on machine learning algorithms where the required compositions and machining parameters would be selected using Artificial Intelligence. Validation of the developed methodology will be conducted using state of the art machining and cutting tools under relevant mechanical loads. This project is closely linked with the activities in the Composite Research Centre and Advanced Manufacturing Research Centre at The University of Sheffield. 

Engineering (12)

Funding Notes

This project is available only for Self funded students.

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