Robustness-performance optimisation for automated composites manufacture

Completed

Renewable Energy Sources(Wind Energy)
Energy Efficiency(Transport)
Not Energy Related

Basic and strategic applied research

PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials)
PHYSICAL SCIENCES AND MATHEMATICS (Applied Mathematics)
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering)

Not Cross-cutting

Dr AA Skordos
School of Applied Sciences
Cranfield University

Standard

EPSRC

11 November 2013

10 November 2016

36 months

£777,199

Materials processing

East of England

Manufacturing : Maths

Dr AA Skordos, School of Applied Sciences, Cranfield University

Professor FG Ball, Mathematical Sciences, University of Nottingham
Professor KA Cliffe, Mathematical Sciences, University of Nottingham
Dr IA Jones, Mechanical, Materials and Manufacturing Engineering, University of Nottingham
Professor D Lesnic, Applied Mathematics, University of Leeds
Professor A Long, Mechanical, Materials and Manufacturing Engineering, University of Nottingham
Dr J Mehnen, School of Applied Sciences, Cranfield University
Dr K Potter, Aerospace Engineering, University of Bristol
Dr P Schubel, Mechanical, Materials and Manufacturing Engineering, University of Nottingham
Professor M Tretyakov, Mathematical Sciences, University of Nottingham

Project Contact, Coriolis Composites Technologies SAS, France
Project Contact, ESI UK Ltd

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.

17/03/14