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| Reference Number | UKRI1890 | |
| Title | Co-AIMS: Research Hub on Collaborative AI for Manufacturing Sustainability | |
| Status | Started | |
| Energy Categories | Not Energy Related 20%; Energy Efficiency (Industry) 80%; |
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| Research Types | Basic and strategic applied research 100% | |
| Science and Technology Fields | PHYSICAL SCIENCES AND MATHEMATICS (Statistics and Operational Research) 30%; PHYSICAL SCIENCES AND MATHEMATICS (Computer Science and Informatics) 40%; ENGINEERING AND TECHNOLOGY (Mechanical, Aeronautical and Manufacturing Engineering) 30%; |
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| UKERC Cross Cutting Characterisation | Not Cross-cutting 50%; Sociological economical and environmental impact of energy (Technology acceptance) 50%; |
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| Principal Investigator |
Niels Lohse University of Birmingham |
|
| Award Type | Standard | |
| Funding Source | EPSRC | |
| Start Date | 31 October 2025 | |
| End Date | 31 October 2032 | |
| Duration | 84 months | |
| Total Grant Value | £12,073,177 | |
| Industrial Sectors | Unknown | |
| Region | West Midlands | |
| Programme | Manufacturing and the Circular Economy | |
| Investigators | Principal Investigator | Niels Lohse , University of Birmingham |
| Other Investigator | Seemal Asif , Cranfield University David Butler , University of Birmingham Jack Chaplin , University of Nottingham Kerstin Eder , University of Bristol Fern Eldson-Baker , University of Birmingham John Erkoyuncu , Cranfield University Sabine Hauert , University of Bristol Tom Hodgson , University of Sheffield Karol Janik , The Manufacturing Technology Centre Ltd Emma Kendrick , University of Birmingham Ales Leonardis , University of Birmingham Giovanna Martinez Arellano , University of Nottingham Paolo Missier , University of Birmingham Bernadin Namoano , Cranfield University Samia Nefti-Meziani , University of Birmingham Duc Pham , University of Birmingham Ingmar Posner , University of Oxford Mostafizur Rahman , The Manufacturing Technology Centre Ltd Svetan Ratchev , University of Nottingham Ashutosh Tiwari , University of Sheffield Philip Webb , Cranfield University |
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| Web Site | ||
| Objectives | ||
| Abstract | Manufacturing plays a key part for achieving and going beyond NetZero by 2050. Manufacturing accounts for 14% of the UK territorial greenhouse gas (GHG) emissions and will be vital for making and maintaining the emerging technologies underpinning the green transition. Labour productivity is crucial to achieve the ambitions 2050 targets while growing our living standard with an aging population and low birth rate. UK Growth has been sluggish falling behind many peer economies. Manufacturing’s drive for connectivity and data has laid the foundation for digital technologies. However, the full gains in productivity and efficiency, can be realised only by incorporating artificial intelligence (AI). Optimising, simultaneously: productivity, resilience, and sustainability exceeds human capacity. The advancement of AI offers unparalleled potential for manufacturing, by empowering people. Examples include adapting to radical demand & supply changes, managing complex sets of human and machine resources, or dexterously adapting to in-process variations. Humans and AI offer co-working, with automation could supercharge productivity by +36% while reducing emissions below 1990 levels. The vision: Position the UK as a world leader in research and commercialisation of AI-empowered autonomous machines and systems to transform manufacturing productivity and sustainability for a net positive future by empowering people. The aim: Deliver, collaboratively with manufacturing businesses, leading-edge technologies for robust, safe, trust-worthy, fault-tolerant, and co-operative autonomous AI systems for manufacturing and establish a platform for their design, development, testing, and validation. Key objectives: defined with our stakeholders (users, providers, governance) to achieve future sustainable manufacturing and surpass NetZero by 2050: Eliminate all waste and emissions in complex, interdependent manufacturing ecosystems (aiming for zero emissions and waste, zero downtime, double life expectancy). Enable safe and meaningful work with AI systems, empowering workers to contribute their skills regardless of their location – whether remotely or on-site - and physical ability. Supercharge productivity by increasing the autonomy of AI-powered machines (+40%). Enhance resilience and agility in decentralised, circular, interconnected production systems and networks (reduce time to recovery -50%, towards zero setup and changeover time, towards batch size 1). UK flagships to act as lighthouses (four+ sectors) to facilitate the exchange of expertise and knowledge between industrial experts, factory workers and academic researchers. To address these objectives, the hub will focus on three research themes focusing at three levels of manufacturing ecosystems: AI-powered machines for agile and effective task execution Self-optimising AI for agile production systems AI-enhanced logistics and Total Quality Control for distributed, circular production networks Five research priorities will investigate the underpinning AI challenges across the themes: Edge AI for real-time digitisation of manufacturing ecosystems Adaptive skill learning and Human-AI collaboration for advanced manufacturing ecosystems Hybrid collective AI for distributed design, planning, and control of manufacturing ecosystems Verification and validation for safe and reliable AI in manufacturing Environmental, social, and economic context of AI in manufacturing To unlock the transformative effect of AI and rapidly move manufacturing beyond NetZero, the hub will work with key industrial sectors including automotive, aerospace, clean energy, and food&drink to setup flagship test and demonstration ecosystems. These will act as light houses for the wider industry to showcase best in class applications of AI to eliminate waste and emissions, enable safe and flexible working, supercharge productivity, and enhance the resilience and agility of critical processes | |
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| Added to Database | 07/01/26 | |
