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Co-AIMS: Research Hub on Collaborative AI for Manufacturing Sustainability

Lead Research Organisation: UNIVERSITY OF BIRMINGHAM

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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