Completed Materials & Manufacturing Chemistry

EPSRC Centre for Innovative Manufacturing in Composites

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AI plain-English summary

A factory robot lays down carbon fibre tape with millimetre precision, while a computer model simultaneously predicts where wrinkles or voids might form in the finished part. This EPSRC Centre aims to turn composite manufacturing—currently slow, costly, and prone to defects—into a fast, reliable, and automated process. The problem is that high-performance composites, used in aircraft wings, wind turbine blades, and lightweight car bodies, are still largely handmade. That limits production rates and drives up costs, keeping these materials out of many mass-market applications. The Centre’s two research themes tackle this from both sides: one develops computer models that simulate the entire manufacturing process, from fibre deposition through curing, to predict and prevent defects before they happen; the other builds and tests next-generation hardware—robotic tape placers, high-speed preforming rigs, and rapid heating tools—that can produce complex composite parts in minutes rather than hours. If successful, the work could make lightweight, fuel-saving composites as cheap and fast to produce as steel or aluminium, transforming industries from aerospace to automotive to renewable energy.

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The EPSRC Innovative Manufacturing Centre in Composites will conduct a programme of fundamental manufacturing research comprising two research themes aimed at developing efficient, high rate, low cost and sustainable manufacturing processes coupled to effective and validated design and process modelling tools. These processes will aim to deliver high yield, high performance and high quality components and structures. The themes are as follows:Theme 1: Composites Processing ScienceThe focus for this theme is to develop integrated modelling systems for predicting and minimising process induced defects and defining and optimising process capability. Topics include: Multi-scale process modelling framework for candidate processes (fibre deposition, resin infusion, consolidation and cure); Stochastic simulation of process and resulting material/structure variability, leading to prediction of process induced defects at the macro, meso and micro scales; Analysis of design/ manufacturing/ cost interactions, enabling process capability mapping, design and process optimisationTheme 2: Composites Processing TechnologyThe focus for this theme will be experimental investigation of next-generation, high rate processing technologies as essential elements within a flexible composites manufacturing cell with multi-process capability. Topics include: Development of rapid deposition technologies: automated robotic control for tow/tape placement, development of flexible/ hybrid systems, application to dry fibre and thermoplastic composites manufacture; High speed preforming processes: fibre placement, Discontinuous Carbon Fibre Preforming (DCFP), multiaxial and 3D textiles and their automated integration into multi-architecture, multi-functional composites; High rate & controlled thermal processing: rapid heating/curing and innovative tooling; Process and parts integration with novel joining technologies, tolerance reduction and on-line inspection In addition to the main research themes, the platform element within the Centre will support four generic research projects operating across the Centre to develop common technologies and underpin the main research priorities. These technology areas are: Multi-scale modelling; Cost modelling; Automation/robotics; and, Design and manufacturing quality integration.

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Researchers

Alexandros Skordos (Co-Investigator)Andrew Mills (Co-Investigator)Andy Long (Principal Investigator)Constantinos Soutis (Co-Investigator)Ivana Partridge (Co-Investigator)Kevin Potter (Co-Investigator)Michael Johnson (Co-Investigator)Michael Wisnom (Co-Investigator)Nicholas Warrior (Co-Investigator)Peter Schubel (Co-Investigator)Prasad Potluri (Co-Investigator)SJ Eichhorn (Co-Investigator)Stephen Hallett (Co-Investigator)Stephen Pickering (Co-Investigator)

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Original classification

Research Grant

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