High-Volume Composites Manufacturing Cell with Digital Twinning Capability

Lead Research Organisation: University of Nottingham
Department Name: Faculty of Engineering

Abstract

The use of composite materials has increased substantially over recent years, leading to projected UK sector growth from £1.5b to £12b by 2030. Much of this potential is associated with lightweighting of vehicles, delivery of durable structures for renewable energy and infrastructure, and next generation single aisle civil aircraft. These all have the potential to make an immediate and positive impact on both the UK's climate change and infrastructure targets, in addition to direct impact on the economy through jobs and exports. However, realising these targets depends primarily on the ability of the industry to deliver structures at volumes and quality levels demanded by these target applications.

In order to meet these challenges, we seek to develop a High-Volume Composites Manufacturing Cell with Digital Twinning Capability (HV-COMMAND). The cell features four components and is configured to facilitate research into each stage of the composite compression manufacturing process (design, handling, forming and inspection). HV-COMMAND cell will therefore deliver an end-to-end replication of industrial automated composites manufacture whilst retaining the size and flexibility requirements to operate within stretch targets appropriate to a research setting.

The data-rich combination of stages within the cell will ultimately deliver a virtual duplication of the manufacturing process - a 'digital twin' capturing the effect of material and process variabilities during forming. This will facilitate future process developments, permitting high-risk feasibility studies whilst mitigating risk of damage to experimental equipment.

Planned Impact

We seek to make a major contribution to the UK composites manufacturing industry by providing greater understanding of the manufacturing and simulation processes relevant to sectors requiring high volumes of composite components (e.g. 100,000ppa in the automotive sector). This will greatly enhance efficiency and quality and therefore open up many more applications of these materials. This in turn will contribute to the GDP of the UK in this rapidly expanding area. UoN have considerable experience in working with and delivering successful projects with members of the automotive industry such as Jaguar Land Rover and Ford, but also with other companies striving to increase productivity, such as Bentley, Aston Martin Lagonda, GKN and McLaren.

This High-Volume Composites Manufacturing Cell with Digital Twinning Capability (HV-COMMAND) will contribute to advancements across a range of composites manufacturing sub-disciplines. This will enable fundamental research in a number of areas within composites manufacturing science including: Material deposition (e.g. handling, ply assembly, fibre architecture), Moulding (e.g. Resin transfer Moulding, compression moulding, out-of-autoclave processing), NDT/Inspection (e.g. defect prediction, fibre alignment), Simulation (e.g. process optimisation, design for manufacture), Recycling (e.g. recyclate conversion), Automation (simulation-based process control), and Digital Manufacturing (digital twinning). These applications are also relevant to a range of industry sectors including automotive, aerospace, rail, renewable energy, construction and marine ensuring a number of possible routes for industrial exploitation of the research enabled by the cell.

The UK and international academic community will benefit from the high quality research output generated by one of the leading composites manufacturing research groups in the country. The UoN Group has a high standing within the international academic community, and an excellent track record of publication in high impact factor journals. We also seek to encourage access to HV-COMMAND to external institutions, facilitating excellent collaborative research, especially through CIMComp whose 10 member universities account for over 80% (£24.6m) of the current EPSRC composites portfolio (£29.9m), include 17 professorial groups and over 40 researchers and postgraduates

The UoN Group attends the major international conferences (ICCM, SAMPE), and collaborates with the leading groups around the world (e.g. Leuven, US National Labs, CRCAS, Australia). UoN also organises the ICMAC (International Conference on Manufacturing of Advanced Composites) conference biannually which offers further opportunities for dissemination. Both Warrior (PI), Turner (Co-I) and Harper (Co-I) sit on the Composites Leadership Forum through sub-committees (Technology Working Group, Sustainability Working Group and Automotive Working Group), and Harper sits on the SAMPE (Society for the Advancement of Material and Process Engineering) UK and Ireland committee. Through CIMComp, we also possess funding for international missions which will enable dissemination of the HV-COMMAND capabilities and outputs to key stakeholders outside of the UK. This will also enable relevant learning to be incorporated into the research undertaken within the cell, ensuring that the benefit is shared in both directions.

The HV-COMMAND cell will enable researchers both at UoN at other institutions to engage with the UK composites community to provide internationally leading manufacturing techniques and the supporting experimental infrastructure to validate the data, empowering the UK manufacturing supply chain.

Publications

10 25 50
 
Description Hub launched 2020 Annual Report 
Form Of Engagement Activity A magazine, newsletter or online publication
Part Of Official Scheme? No
Geographic Reach International
Primary Audience Industry/Business
Results and Impact The Hub released a formal Annual Report for 2020, detailing all of the Hub related research and developments over the course of 2019- 2020. This report was also published on the Hub website to target all audiences.
Year(s) Of Engagement Activity 2020
URL https://cimcomp.ac.uk/wp-content/uploads/pdf/CIMComp%20Annual%20Report%202019-2020.pdf