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PhD in Tomography-based structural simulation for hybrid architecture carbon fibre composites

PhD in Tomography-based structural simulation for hybrid architecture carbon fibre composites

Project Overview


Carbon fibre-reinforced composites offer an exceptional strength-to-weight ratio, which makes them attractive for structural applications in many industries. However, fibre length is critical to leverage the strength characteristics of carbon fibres, and manufacturing complexity increases with fibre length. For example, continuous fibre composites provide exceptional mechanical properties, while discontinuous fibres provide manufacturing flexibility but with a penalty in strength. Hybrid architecture composites that combine continuous and discontinuous fibres in a single component have attracted growing interest in automotive and aerospace applications as they can provide a balance between mechanical properties and processability.

This project aims to develop an image analysis tool for quantifying fibre orientation and fibre content distributions from X-ray computed tomography (XCT) for hybrid architecture carbon fibre composites, and subsequently develop a model linking the material’s meso-structure to mechanical properties to create structural simulation models. XCT will be used to produce three-dimensional (3D) computer models of the imaged volume of a hybrid composite sample.

Current challenges:

  1. Although commercial image analysis software can map the fibre orientation distribution from XCT scans, they cannot distinguish different reinforcement phases in hybrid architecture composites
  2. Existing structural analysis is mostly performed by assuming a homogeneous fibre orientation and fibre content distribution, where the influence of the manufacturing process is not considered. The discrepancy between the as-designed and as-manufactured geometries may induce non-negligible effects.
  3. Few studies have utilised process simulation to predict the fibre architecture after moulding, taken into account the influence of manufacturing induced fibre re-orientation and re-distribution. However, due to the lack of experimental methods for quantifying fibre architecture in these materials, the predictive validity of current process simulation models is questionable.
  4. Existing process simulation models only predict the fibre orientation. They do not consider the large deformation of fibre tows caused by high pressures applied in a high-rate compression moulding process, such as tow spreading/compaction, tow splitting and fibre waviness.

Objectives:

  1. Develop an image segmentation algorithm to separate different reinforcement phases in a hybrid architecture composite
  2. Develop a fibre analysis technique for quantifying fibre orientation and fibre content distributions from XCT scans. Introduce additional fibre architecture parameters such as in-plane tow extensional/compressive strains, fibre waviness, etc.
  3. Determine the mechanical properties of composites as a function of each fibre architecture parameter (orientation, volume fraction, in-plane strains, waviness) experimentally through specifically designed tensile testing samples; and numerically through micro-/meso-scale models generated using the XCT scan data
  4. Perform full-part structural analysis and validate the model against experimental structural testing.

Essential and Desirable Criteria


Essential: Strong background in solid mechanics, good programming skills using Python or MATLAB

Desirable: Knowledge in composites and composites manufacturing, experience in finite element analysis


To apply

To apply please complete our online enquiry form and upload your CV.

Please ensure you meet the minimum requirements before filling in the online form.

For more information please contact, Professor Ton Peijs via email: T.Peijs@warwick.ac.uk.

Key Information:

Funding Source: DTP

Stipend: Standard UKRI stipend for 4 years

Supervisors: Dr Jay Warnett, Prof Ton Peijs

Eligibility: Available to eligible Home fee status and UK domicile EU students

Start date: 30th September 2024