Host Institution: The University of Bristol
Start Date: 1st September, 2022
Duration: 12 months
Lead Investigator: Jonathan Belnoue
Co-Investigators: Lee Harper, Stephen Hallett
The aim of this project is to find a practical and efficient way to combine thermoforming and injection moulding simulation tools to model the temperature and rate dependency effects encountered during thermoplastic injection overmoulding. A particular emphasis of the project will be to capture the deformation of the oraganosheet throughout the process, starting with the initial thermoforming phase and including the influence of the injection overmoulding phase.
Novelty: Current thermoforming simulation tools are computationally inefficient and cannot be integrated with existing injection moulding simulation tools to model the combined process effects. The influence of the short-fibre injection phase on the integrity of the organosheet insert is commonly overlooked. This project sets to develop new numerical tools that can efficiently support the design and manufacture of overmoulded thermoplastic composites, building upon existing expertise and constitutive models available at the University of Bristol (UoB) and the University of Nottingham (UoN).
Timeliness: Wider adoption of the injection overmoulding process will help to reduce the sector’s dependency on thermoset-based materials for producing structural components, offering a more sustainable thermoplastic-based alternative that can be more readily recycled. In addition, injection moulding compounds readily use short fibres (<5 mm), providing a circular economy for thermoplastic composites waste.
Transformative Aspects: Thermoplastic injection overmoulding has the potential to offer aerospace quality components at automotive production rates, but the effective combination of these two manufacturing processes makes component variability a concern. Thermoforming and injection moulding both share complex time, temperature
and pressure dependencies, which make overall process optimisation difficult by trial and error. A dedicated process model will avoid costly mistakes and allow a reduction in
process variability through identification of robust process parameters. It will thus unlock more rapid industrial uptake of this high-rate deposition process, offering production volumes exceeding 100,000 parts per annum.