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Projects: Projects for Investigator
Reference Number EP/K031430/1
Title Robustness-performance optimisation for automated composites manufacture
Status Completed
Energy Categories Renewable Energy Sources(Wind Energy) 5%;
Energy Efficiency(Transport) 5%;
Not Energy Related 90%;
Research Types Basic and strategic applied research 100%
Science and Technology Fields PHYSICAL SCIENCES AND MATHEMATICS (Metallurgy and Materials) 50%;
ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 30%;
UKERC Cross Cutting Characterisation Not Cross-cutting 100%
Principal Investigator Dr AA Skordos
No email address given
School of Applied Sciences
Cranfield University
Award Type Standard
Funding Source EPSRC
Start Date 11 November 2013
End Date 10 November 2016
Duration 36 months
Total Grant Value £777,199
Industrial Sectors Manufacturing
Region East of England
Programme Manufacturing : Maths
Investigators Principal Investigator Dr AA Skordos , School of Applied Sciences, Cranfield University (99.991%)
  Other Investigator Dr J Mehnen , School of Applied Sciences, Cranfield University (0.001%)
Professor KA Cliffe , Mathematical Sciences, University of Nottingham (0.001%)
Professor FG Ball , Mathematical Sciences, University of Nottingham (0.001%)
Professor M Tretyakov , Mathematical Sciences, University of Nottingham (0.001%)
Dr K Potter , Aerospace Engineering, University of Bristol (0.001%)
Professor D Lesnic , Applied Mathematics, University of Leeds (0.001%)
Dr IA Jones , Mechanical, Materials and Manufacturing Engineering, University of Nottingham (0.001%)
Professor A Long , Mechanical, Materials and Manufacturing Engineering, University of Nottingham (0.001%)
Dr P Schubel , Mechanical, Materials and Manufacturing Engineering, University of Nottingham (0.001%)
  Industrial Collaborator Project Contact , Coriolis Composites Technologies SAS, France (0.000%)
Project Contact , ESI UK Ltd (0.000%)
Web Site
Abstract 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.
Publications (none)
Final Report (none)
Added to Database 17/03/14