Active Projects

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Manufacturing a better society - How can we make manufacturing where EVERYONE wants to work?

Funder: ECONOMIC AND SOCIAL RESEARCH COUNCIL (ESRC)

Vision - Our vision is to create a new transdisciplinary framework that merges social science and digital manufacturing technology and enables currently marginalised groups to
participate in manufacturing. Our approach will lead to the development of industrial digital
technologies enabling inclusive workplaces where anybody, including the most marginalised groups, are afforded access to meaningful work. Similarly, the development of these inclusive digital technologies will lead to new perspectives on marginalised groups and
methodologies for social science research.
We propose an engaged transdisciplinary approach based on action research cycles, where design interventions (technical and social) will take place with the research team and our disabled partners. The interventions will examine the optimisation of the technology but will also explore the accessibility perceptions of technological use and application in a manufacturing work context. The outcomes of each action research cycle will inform the subsequent phase of the project. 

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EPSRC Network Plus UK Robotics and Autonomous Systems +

Funder: EPSRC                       Academic Partners: Universities of Manchester, Cardiff, Heriot-Watt and Leeds.

Our vision: for UK-RAS+ is to continue to coordinate and strengthen the research community, as well as identify fundamental research challenges that the UK should be addressing, expand the internationalisation of the UK’s research base and provide support to enable early-career researchers (ECRs) achieve their potential. In doing this we aim to create a highly coordinated and focused research community that is world-leading in areas of strategic value to the UK, with programmes and support structures in place to nurture future talent.

As the importance of RAS, as a critical enabling technology across many sectors of importance to the UK, grows, it is vital that UK-RAS continues its work to strengthen the community, and that it addresses existing gaps in network activities to ensure that the community can be world leading in areas which are of national strategic value. 

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A digital COgnitive architecture to achieve Rapid Task programming and flEXibility in manufacturing robots through human demonstrations (DIGI-CORTEX)

Funder: EPSRC                Academic Partners: University of Southampton

Vision: Our aim is to investigate cognitive architectures that equips robots with the capability to rapidly learn new skills by passive observation of a human demonstrating a task to the robot and applying previously learnt skills to new task scenarios, thereby achieving task flexibility on the manufacturing floor. This opens up exciting possibilities. For one, it means that robots can be taught to do various tasks with no intensive programming required by a human. It also means that robots can be flexibly used to perform a wide variety of tasks thereby reducing the need for capital intensive, rigid and time-consuming manufacturing set ups.

There is a gap in literature of applying digital mental models on robots for building in flexible and creative robots that can be flexibly and rapidly re-tasked for various tasks. Nevertheless, there is a growing realisation that creativity is needed in industrial robots of the future and that this could be achieved through providing them with mental models. We investigate a cognitive architecture that embeds the human cognitive capabilities of mental simulation for creative problem solving on manufacturing robots and task structure mapping in a unified framework for the purposes of achieving rapid re-tasking (task flexibility) of industrial robots via passive human demonstrations. 

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NanoMan: Self-Optimising Nanoscale Manufacturing Platforms for Achieving Multiscale Precision

Funder: EPSRC        Academic Partners: Universities of Leeds and Sheffield

Our Vision: In this project we are building platform technologies, comprising advanced chemical reactors underpinned by computational intelligence, which can scale up production of advanced nanoparticle products without loss in the precise control over structural dimensions which are achieved in research laboratories. We are building laboratory reactors which can be programmed to monitor the nanoparticle formation process in real time and relate conditions to the particle properties. Throughout the manufacturing process the machine learning algorithms will direct the reactors towards achieving the desired specification through 'self-optimisation' of conditions.

A critical part of the project is using the data obtained in the lab experiments to build a relationship between process and product which can be transferred onto equipment which can make the materials on a commercially relevant scale in a process called augmented lossless scale-up. We are investigating the optimisation of laboratory nanoparticle formation processes and demonstrating scale in several manufacturing environments, including R&D process laboratories and Commercial manufacturing facilities at our partners sites. Such demonstration will encourage further innovation beyond the lifetime of the project which can work towards realising advanced materials currently confined to research laboratories.

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An intelligent digital framework to speed up lipid nanoparticle (LNP) drug delivery systems manufacture

Funder: EPSRC    Academic Partner: University of Sheffield

Vision: Lipid nanoparticles (LNPs) gained considerable interest as drug delivery systems for leading nucleic acid vaccine candidates for COVID-19, such as the mRNA vaccines. LNP vaccines and therapeutics offer the advantage to move from development to clinical trials on much faster timescales than more traditional vaccines and therapeutics, adding to the resilience towards novel viruses in the future.

Despite a strong UK research base and a growing industrial footprint, there are clear gaps around efficient scale-up, manufacture and analysis of LNP based vaccines, which is a key objective of CPI. To solve these challenges, we will apply Digital Twins and Artificial Intelligence (AI) to optimise the precise manufacture of LNP-RNA vaccines. Digital Twins will enable us to replicate processes without the need for expensive physical experiments while the AI enables us to find optimal parameters for a specific set of inputs. Our work aims to overcome the current inefficient and wasteful evaluation and scale-up processes for encapsulation and purification – allowing pharma companies to get potential medicines into clinical trials faster and at lower cost.

We will use several industrially relevant microfluidic flow reactors and experiments to validate the Digital Twins and AI. We will also add sensors (e.g. pressure, temperature) to gather data for the AI and help understand how the mixing process could be accurately modelled in a Digital Twin.

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Leap for Life

Funders: ESRC/EPSRC/UKRI/Future Leaders Network     Academic Partners: UCL, Imperial & University of Leeds

Our vision: Salmon are one of the most popular and iconic fish. Not only are they a staple on our dinner plates, but also the story of wild salmon’s epic migration – their heroic swim upstream in their river of origin after spending several years in the open seas continues to captivate our imagination.

What often goes unnoticed, however, is the alarming decline of wild salmon globally. What we typically find in supermarkets is usually farmed salmon rather than wild caught fish. This is certainly the case for Atlantic salmon (Salmo salar), native to the North Atlantic Ocean, which breeds in rivers in the UK, Europe, and North America.

Although there is a good scientific understanding of the drivers of wild Atlantic salmon decline, little progress has been made in reversing population declines. Some threats to wild salmon populations are faced in the ocean, including climate change impacting water temperatures, and fishing pressures. Tackling these systemic challenges to salmon survival requires trans-national and trans-disciplinary cooperation.

Our project focusses on the challenges of wild Atlantic salmon conservation. We bring together scholars from the diverse disciplines of history, literature, law, genetics, conservation, chemistry, synthetic biology, and robotics.

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