Characterisation of elastomeric compounds in FEA and development of techniques

  • Mizan Kapade

Student thesis: Master's Thesis


Elastomers are non-linear in their physical properties. This thesis examines the various methods of describing hyperelastic material behaviour. The model chosen for use was the Arruda-Boyce model due to its flexibility to use minimal input data to simulate and retrieve indicative results.

The types of material to be modelled were characterised by a series of uniaxial deformation tests and volumetric compressive test. Uniaxial compression tests were conducted to gather stress-strain response curves. The combination of volumetric and uniaxial compression tests helped generate a feasible material model for 2 different natural rubber-based compounds. The gathered data was validated using non-linear FEA.

Finite Element Analysis was used to create a working simulation of an engine mount. The engine mount problem was a combination of contact conditions between different materials with concerns of material self-contact. The results indicated the unlikelihood of static failure, whilst concerns of dynamic failure remained.

Finite Element Analysis was also used to simulate a working model for a high deformation track-pin system with the rubber section depth undergoing high deformation. Modelling challenges such as changing contact conditions, high deformation and material slippage caused due to loss of contact were encountered. The results indicated the most appropriate approach for solving this in 2D axisymmetric and 3D. An iterative design approach was used to suggest a philosophy change in the design whilst following the design brief.

It was concluded that Finite Element Analysis would be beneficial for PPE as it would allow the engineering to move away from resource extensive prototyping and utilise computational resources. There is also a need for building upon the foundational procedures in acquiring material response curves. Doing so would allow PPE to create a material library for simulation purposes, adding to their expertise.
Date of Award6 Oct 2022
Original languageEnglish
SupervisorJohn Allport (Main Supervisor) & Simon Barrans (Co-Supervisor)

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