The design of products to achieve acceptable levels of noise and vibration is a major concern across a range of industries. In many cases there is a large trade off between cost and performance, and this means that achieving an efficient design is crucial to commercial success. In principle design optimisation can be achieved through testing and improving physical prototypes, but the production of a prototype is time consuming and costly. For this reason there is a pressing need for virtual design methodologies, in which computational models are used to produce a near-final design before a physical prototype is built. Computational models used for noise and vibration analysis must be able to predict the performance of the system over a wide frequency range, potentially ranging from low frequency vibration problems at several hertz to high frequency noise problems at several kilohertz, and this presents severe difficulties. High frequency motions require a very detailed computer model, and this leads to long run times that are not ideal for iterative design. Furthermore, the high frequency performance of a system can be very sensitive to small manufacturing imperfections, and hence the predicted performance may not match the performance of the actual system. These difficulties can be largely overcome by employing recent advances in noise and vibration modelling in which a technique known as Statistical Energy Analysis (SEA) is combined with more conventional analysis methods such as the finite element method (FEM) or the boundary element method (BEM); this approach is known as the Hybrid Method. The Hybrid Method leads to a very large reduction in the run time of the model, while also providing an estimate of the variance in the performance caused by manufacturing imperfections. However, this approach does not fully solve the prediction problem, as a further major difficulty remains: some components in a system can be so complex that it is not possible to produce a detailed computational model of the component, and hence some degree of physical testing is unavoidable. Frequently experimental measurements are used to validate a computational model, or to update the parameters in a computational model, but the requirement here is quite different: the measured data must be used to complete the computational model by coupling a representation of the missing complex component to the other parts of the model. This issue forms the core of the current research proposal. The aim of the present work is to add "experimental" components to the Hybrid Method, and one way to do this is to model a component as a grey or black box: a grey box model consists of mathematical equations with experimentally determined parameters, while a black box model is based purely on measured input-output properties. These models must be capable of being coupled to either FEM, BEM, or SEA component models, and the project will address this issue. A major challenge is to determine the appropriate experimental tests and machine learning algorithms that are required to produce such models in the context of complex vibro-acoustic components. A second major challenge is to quantify the uncertainty in such models, and to include this uncertainty in the combined system model. The model must predict outputs that are useful to the designer, and such outputs include noise and vibration levels, together with uncertainty bounds on the predictions. In some cases "sound quality" rather than the overall noise level is of concern, and the project will develop techniques for the "auralisation" of the output of the combined model. A number of case studies will be developed with industrial partners to explore the application of the proposed approach. The present research programme will produce an efficient and reliable vibro-acoustic "design by science" prediction tool that meets the needs of a wide range of industrial sectors.

The UK automotive industry is a large and critical sector within the UK economy. It accounts for 820,000 jobs, exports finished goods worth £8.9bn annually and adds value of £10bn to the UK economy each year. However, the UK automotive industry is currently facing great challenges, such as responsibility for a 19% and growing share of UK annual CO2 emissions, strong international competition, declining employment and hollowing-out of the domestic supply chain, and enormous pressure from regulatory bodies for decarbonisation. A solution to these challenges comes from the development and manufacture of low carbon vehicles (LCVs), as identified by the UK government. Vehicle lightweighting is the most effective way to improve fuel economy and to reduce CO2 emissions. This has been demonstrated by many vehicle mass reduction programmes worldwide. Historically vehicle mass reduction has been achieved incrementally by reducing the mass of specific vehicle parts piece-by-piece, with little consideration of the carbon footprint of input materials and closed-loop recycling of end of life vehicles (ELVs). Our vision is that the future low carbon vehicle is achieved by a combination of multi-material concepts with mass-optimised design approaches through the deployment of advanced low carbon input materials, efficient low carbon manufacturing processes and closed-loop recycling of ELVs. To achieve this vision, we have gathered the best UK academic brainpower for vehicle lightweighting and formed the TARF-LCV consortium, whose members include 8 research teams involving 18 academics from Brunel, Coventry, Exeter, Imperial, Manchester, Nottingham, Oxford Brookes and Strathclyde. TARF-LCV aims to deliver fundamental solutions to the key challenges faced by future development of LCVs in the strategic areas of advanced materials, enabling manufacturing technologies, holistic vehicle design and closed-loop recycling of ELVs. We have developed a coherent research programme organised in 6 work packages. We will develop closed-loop recyclable aluminium (Al) and magnesium (Mg) alloys, metal matrix composites (MMCs) and recyclable polymer matrix composites (PMCs) for body structure and powertrain applications; we will develop advanced low carbon manufacturing technologies for casting, forming and effective vehicle assembly and disassembly; and we will develop mass-optimised design principles and specific life cycle analysis (LCA) methodology for future LCV development. To deliver the 4-year TARF-LCV programme, in addition to the EPSRC funding requested, we have leveraged financial support for 2 post-doctoral research fellows from the EPSRC Centre-LiME at Brunel University and LATEST2 at Manchester University, and for 9 PhD studentships from partner universities. Consequently, the TARF-LCV research team will include 18 academics, 11 post-doctoral research fellows and 18 research students. This not only ensures a successful delivery of the TARF-LCV research programme, but also provides a training ground for the future leaders of low carbon vehicle development in the UK.

The theme area is manufacturing of engineering composites structures, specifically those which comprise continuous high performance fibres held together with a polymeric matrix. The relevant industry areas include aerospace, automotive, marine, wind energy and construction. The proposal demonstrates continuing and growing need in the UK polymer composites manufacturing sector for suitably technically qualified individuals, able to make positive and rapid impact on its international manufacturing competitiveness. Extension of a newly created Industrial Doctorate Centre in Composites Manufacture fills an existing gap in provision of industrially focussed higher level education in the UK, in the specialist discipline of polymer composites manufacturing. It has its centre of gravity in Bristol, with the rapidly expanding National Composites Centre (NCC) the natural home-base for the cohorts of composites manufacturing Research Engineers embedded in the composites manufacturing industry. This new hub of applied research activity focussed at TRL 3-5 is different from but highly complementary to the outputs of composites manufacturing PhD students within the EPSRC Centre for Innovative Manufacturing in Composites (CIMComp), working on more fundamental research topics in composites manufacture at TRL 1-3. Achieving a clearer definition of the industrial composites manufacturing challenges and of new knowledge base requirements will provide direction for the industrially relevant accompanying fundamental research. The EPSRC Centre for Innovative Manufacturing in Composites has established and maintains close management overview of this IDC , as well as fostering links with related CDTs within the wider High Value Manufacturing Catapult, initially specifically the AMRC Composites Centre IDC in Machining Science. Over time such connections will establish a critical mass of industrially focussed manufacturing research activity in the UK, raising the national and international status of the EngD brand in the composites industry, in academia and in professional institutions by targeted dissemination through CIMComp in conjunction with the NCC

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.

Society complexity and grand challenges, such as climate change, food security and aging population, grow faster than our capacity to engineer the next generation of manufacturing infrastructure, capable of delivering the products and services to address these challenges. The proposed programme aims to address this disparity by proposing a revolutionary new concept of 'Elastic Manufacturing Systems' which will allow future manufacturing operations to be delivered as a service based on dynamic resource requirements and provision, thus opening manufacturing to entirely different business and cost models. The Elastic Manufacturing Systems concept draws on analogous notions of the elastic/plastic behaviour of materials to allow methods for determining the extent of reversible scaling of manufacturing systems and ways to develop systems with a high degree of elasticity. The approach builds upon methods recently used in elastic computing resource allocation and draws on the principles of collective decision making, cognitive systems intelligence and networks of context-aware equipment and instrumentation. The result will be manufacturing systems able to deliver high quality products with variable volumes and demand profiles in a cost effective and predictable manner. We focus this work on specific highly regulated UK industrial sectors - aerospace, automotive and food - as these industries traditionally are limited in their ability to scale output quickly and cost effectively because of regulatory constraints. The research will follow a systematic approach outlined in to ensure an integrated programme of fundamental and transformative research supported by impact activities. The work will start with formulating application cases and scenarios to inform the core research developments. The generic models and methods developed will be instantiated, tested and verified using laboratory based testbeds and industrial pilots (S5). It is our intention that - within the framework of the work programme - the research is regularly reviewed, prioritised and and flexibly funded across the 4 years, guided by our Industrial Advisory Board.