AI improved advanced manufacturing and production processes in factories (RIA) (Made in Europe and AI, Data and Robotics partnerships)

Expected Outcome

• Increased competitiveness and productivity, through innovative AI-enabled advanced manufacturing processes and operations, including real-time monitoring and adaptive optimisation; and
• Reduction of emissions and alignment with Clean Industrial Deal objectives.

Scope

AI approaches in manufacturing processes hold the potential to significantly enhance circularity, process and operational efficiency as well as sustainability of modern factories. Current state-of-the-art technologies have already paved the way for more streamlined operations, yet there remains untapped value in e.g. quality improvement, definition of optimal process operating conditions, reduction of scrap, optimization of energy usage. Real-time monitoring and adaptive optimisation using AI models can enable agile responses to production variability and support sustained, high-performance operations.

New solutions based on innovative enabling technologies such as deep learning, large language models, digital twins, synthetic data, and data-driven models allow manufacturers to improve production system efficiency, elevate product quality, and proactively address critical challenges in energy consumption and carbon footprint. This dual focus on operational excellence and sustainability ensures that factories can maintain competitive advantage while also contributing to specific environmental goals, e.g. reducing the pressure on ecosystems and natural resources. Since innovation capacity and competitiveness also requires a systemic understanding of an organization’s value creating structure, novel AI solutions should be implemented such that they can support all structures and phases of operation, in technical and non-technical terms.

Proposals should produce dedicated innovative explainable AI based solutions in advanced manufacturing for at least two of the following:

• improve processes and operational efficiency, and reduce climate and environmental impact of processes and factories through dynamic selection of optimal processes and production parameters, exploiting AI for process modelling and/or optimisation;
• avoid the production of defective parts using AI to detect process drift and anomalies and correct proactively defects in real time; and
• maximise the fraction of regenerated components or materials used in the production using AI to optimise the material flow.

Proposals should demonstrate potential for these AI tools to adapt to changing production needs and real-time data, and should describe how industrial data access, confidentiality, and secure data-sharing will be addressed.

Projects are encouraged to link with AI Factories, including the Data Labs. The results may be validated in Testing and Experiment Facilities (TEFs), and further deployed via European Digital Innovation Hubs (EDIHs). This topic is linked to the Apply AI Strategy, therefore proposals should seek collaboration with relevant initiatives.

Proposals can optionally address the conditions for implementing the novel AI solutions within an organisations structure and value creating models, thereby contributing to systemic approach of implementing a smart organisation.

This topic is linked to the Apply AI Strategy, therefore proposals should seek collaboration with relevant initiatives.

Proposals should include a business case and exploitation strategy, as outlined in the introduction to Destination ‘Leadership in materials and production for Europe’.

This topic implements the co-programmed European Partnerships Made in Europe and AI, Data and Robotics.

Technology Readiness Level - Technology readiness level expected from completed projects

Activities are expected to start at TRL 4-5 and achieve TRL 6 by the end of the project - see General Annex B.