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Details of Grant 

EPSRC Reference: EP/K031430/1
Title: Robustness-performance optimisation for automated composites manufacture
Principal Investigator: Skordos, Dr AA
Other Investigators:
Schubel, Professor P Cliffe, Professor KA Jones, Dr IA
Potter, Professor K Iglesias, Dr M Ball, Professor FG
Long, Professor A Lesnic, Professor D Tretyakov, Professor M
Mehnen, Professor J
Researcher Co-Investigators:
Project Partners:
Coriolis Composites SA ESI
Department: School of Water, Energy and Environment
Organisation: Cranfield University
Scheme: Standard Research
Starts: 11 November 2013 Ends: 10 November 2016 Value (£): 777,199
EPSRC Research Topic Classifications:
Manufacturing Machine & Plant Materials Processing
Non-linear Systems Mathematics Numerical Analysis
Statistics & Appl. Probability
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
19 Feb 2013 Maths-Manufacturing Call Prioritisation Panel Announced
Summary on Grant Application Form
This project focuses on the development of a manufacturing route for composite materials capable of producing complex components in a single process chain based on advancements in the knowledge, measurement and prediction of uncertainty in processing. The methodology proposed uses measurements of the instantaneous state of a component during production, predictive modelling of associated variability and numerical optimisation. These three are integrated in a control loop that allows the process to adapt in real time in order to compensate for deviations from its nominal state due to variability. This manufacturing philosophy accepts the existence of variability in these highly heterogeneous and directional materials and uses it in order to improve the product as the process evolves.

The necessary developments comprise major manufacturing challenges, such as the real time measurement of fibre variability in robotic fibre placement and the processing of composite components involving areas of large thickness. These are accompanied by significant mathematical advancements, such as the numerical solution of coupled non-linear stochastic partial differential equations, the inverse estimation of composite properties and their probability distributions in different directions based on real time measurements and the formulation and solution of a stochastic model of the variability in fibre arrangements. The integration of these developments will be carried out on a single process chain of fibre placement, resin infusion and resin cure; however their applicability is generic in the context of manufacturing involving heterogeneous materials and variability.

The outcome of this work will enable a step change in the capabilities of composite manufacturing technologies to be made, overcoming limitations related to part thickness, component robustness and manufacturability as part of a single process chain, whilst yielding significant developments in mathematics with generic application in the fields of stochastic modelling and inverse problems.

Key Findings
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Potential use in non-academic contexts
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Date Materialised
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Further Information:  
Organisation Website: http://www.cranfield.ac.uk