Currently the dominant approach for cooling and lubricating machining processes, such as drilling, milling and turning, is to use emulsion-based coolants (otherwise known as metalworking fluids) at high flow rates. There are many serious environmental, financial and health and safety reasons for reducing industry's reliance on emulsion coolants - an estimated 320,000 tonnes/year in the EU alone, up to 17% of total production costs, and over 1 million people are exposed regularly to the injurious effects of its additives which can cause skin irritation and even cancers. Serious environmental problems are also caused by the up to 30% of coolant that is lost in leaks and cleaning processes and which eventually ends up polluting rivers. These issues have motivated extensive research efforts to identify more sustainable machining processes. There is growing and compelling evidence from preliminary studies that cryogenic machining with supercritical CO2 (scCO2) with small amounts of lubricant (Minimum Quantity Lubrication, MQL, referred to as scCO2+MQL machining) can provide a high-performing and more sustainable alternative. Current knowledge gaps in the relationships between key input and output variables, the reasons for variations in performance and concerns over the release of CO2, are preventing a major uptake of this technology by UK manufacturers. This project aims to test the hypothesis that optimising combinations of CO2 with small amounts of the appropriate lubricant can provide reliable, step-change improvements in the performance and sustainability of machining operations. It will carry out a systematic investigation into the effect of scCO2+MQL on cutting forces, heat and tool wear mechanisms during machining of titanium, steels and composite stacks. It will develop: (a) advanced experimental methods in combination with full-scale machining trials to explore how lubrication and heat transfer affect machining performance; (b) lifecycle assessment and scavenging methods for sustainable re-use of CO2; (c) machine learning methods to predict the relationships between process inputs and outputs and (d) develop an effective and efficient optimisation methodology for balancing competing financial, performance and sustainability objectives in scCO2+MQL machining. These will deliver the knowledge, experimental and modelling methods and software tools that UK industry needs to exploit this enormous as-yet untapped potential. The project will involves staff and postdoctoral research assistants from the Universities of Leeds and Sheffield and the Advanced Manufacturing Research Centres in Sheffield, with advice and guidance from a Project Steering Group comprised of leading international academic and industrial experts. Collectively, the team has the expertise in (a) manufacturing systems and tribology; (b) energy systems and lifecycle assessment; (c) fluid mechanics and heat transfer, and (d) machine learning and optimisation, needed to provide the 'how' and 'why' UK industry needs to reliably achieve or exceed the performance improvements seen in preliminary studies, namely doubling of tool life. We will work with our industrial and business sector collaborators to drive transformations in machining rate, process cost and accompanying safety, environmental and quality metrics for the benefit of the UK's defence, civil nuclear and medical manufacturing industries through the 2020s and beyond.

High value manufacturing is an essential component of the UK economy, contributing strongly to our economic prosperity and engineering status around the world. The growth in high value manufacturing to support aerospace, nuclear and other high integrity engineering components, has placed huge pressure on the rapid delivery of reliable and high quality Non-Destructive Evaluation (NDE) to inspect these parts. Currently, much inspection of safety critical components (sometimes requiring 100% part inspection) is performed manually, leading to significant bottlenecks associated with the NDE. Existing robots typically follow pre-programmed paths making them unsuitable to handle, inspect and disassemble parts with a significant tolerance or variability. A new end-to-end approach is needed, embracing manufacture, transport through factory, parts alignment, parts tracking, and inspection (both surface form metrology and NDE) with the associated high volume data management feeding into the quality and assurance compliance processes. Exactly the same process bottlenecks occur when we translate the problem to the regime of Remanufacturing, hence the integrated approach taken through this proposal. Remanufacturing has been identified as being central to the creation of economic growth in the UK and global markets. With supplies of resources and energy limited, the transition to a low carbon economy with strong emphasis on resource efficiency is key to the UK's Industrial Strategy. Remanufacturing can support this transition by achieving significant impact in all industrial sectors through preventing waste, improving resource management, generating sustainable economic growth, increasing productivity and enhancing competitiveness. AIMaReM (Autonomous Inspection in Manufacturing& Remanufacturing) provides a unique combination of data collection, processing and visualisation tools combined with efficient robot path planning and obstacle avoidance, with a focus on manufacturing inspection (NDE and surface form metrology). The project will deliver an automated, systems integrated solution, that will be of direct benefit to the manufacturing sector to allow faster integrated inspection and parts handling, thus saving time, and reducing costs whilst enhancing quality and throughput.

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.

The future prosperity of the UK will increasingly depend on building and maintaining a resilient and sustainable manufacturing sector that can respond to changing supply and demand by adapting, repurposing, relocating and reusing available production capabilities. The pandemic which emerged in 2020 has influenced our perspective of future manufacturing operations and, in particular, has brought into focus the capacity challenges of delivering critical products and maintaining production in the face of major disruptions. It also accelerated the emerging trend for more localised, greener and cost-competitive indigenous manufacturing infrastructure with the ability to produce a wider set of complex products faster, better and cheaper. To meet the long-term structural and post-pandemic challenges, we need transformative new methods of building and utilising future factories by embracing complexity, uncertainty and data intensity in a dynamic and rapidly changing world. The "Morphing Factory" Made Smarter Centre aims to deliver a platform for next generation resilient connected manufacturing services. It will allow future manufacturing operations to be delivered by ubiquitous production units that can be easily repurposed, relocated and redeployed in response to changing market demand. This vision will be delivered through 3 closely related strands: (1) An underpinning fundamental research programme to define the principles, methods and models for future morphing factories in terms of architecture, topology, configuration methods, IoT digital awareness, in-process monitoring and AI based autonomous control. (50%). (2) A dynamic challenge-driven applied research programme to address emerging industrial needs and validate and demonstrate the results through a set of application studies including smart machining, production integrated 3D printing and autonomous assembly integrated into a common hyperconnected morphing factory cloud (45%). (3) A programme of networking and engagement activities with other ISCF Made Smarter research and innovation centres, industry and the general public to maximise the impact of the research, encourage accelerated technology uptake and increase the public awareness (5%).

We propose a Centre for Doctoral Training in Integrative Sensing and Measurement that addresses the unmet UK need for specialist training in innovative sensing and measurement systems identified by EPSRC priorities the TSB and EPOSS . The proposed CDT will benefit from the strategic, targeted investment of >£20M by the partners in enhancing sensing and measurement research capability and by alignment with the complementary, industry-focused Innovation Centre in Sensor and Imaging Systems (CENSIS). This investment provides both the breadth and depth required to provide high quality cohort-based training in sensing across the sciences, medicine and engineering and into the myriad of sensing applications, whilst ensuring PhD supervision by well-resourced internationally leading academics with a passion for sensor science and technology. The synergistic partnership of GU and UoE with their active sensors-related research collaborations with over 160 companies provides a unique research excellence and capability to provide a dynamic and innovative research programme in sensing and measurement to fuel the development pipeline from initial concept to industrial exploitation.