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Circular TwAIn

AI Platform for Integrated Sustainable and Circular Manufacturing
Funder: European CommissionProject code: 101058585 Call for proposal: HORIZON-CL4-2021-TWIN-TRANSITION-01
Funded under: HE | HORIZON-IA Overall Budget: 7,246,100 EURFunder Contribution: 5,937,360 EUR
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Circular TwAIn

Description

Circular TwAIn will lower the barriers for all the stakeholders in manufacturing and process industry circular value chains to adopt and fully leverage of trusted AI technologies, in ways that will enable end-to-end sustainability, i.e. from eco-friendly product design to the maximum exploitation of production waste across the circular chain. To this end, the project will research, develop, validate and exploit a novel AI platform for circular manufacturing value chains, which will support the development of interoperable circular twins for end-to-end sustainability. Circular TwAIn will unlock the innovation potential of a collaborative AI-based intelligence in production based on the use of cognitive digital twins. Moreover, based on the use of trustworthy AI techniques, Circular TwAIn will enable human centric sustainable manufacturing, fostering the transition towards Industry 5.0. Furthermore, Circular TwAIn will enable the integration and combination of different data from various sources over entire product life cycle considering sustainability aspects. The goal is to create and deliver innovative services among the members of the data ecosystem; these services will be embedded in AI-based Digital Twins, supporting an unambiguous communication when realizing complex services for sustainable manufacturing. The ambition of Circular TwAIn is to unleash the sustainability potential of AI technologies in circular manufacturing chains through: (i) Introducing AI optimizations in stages where AI is still not used (e.g., AI-based product design); (ii) Using AI for multi-stage and multi-objective circular optimizations that could improve sustainability performance. In this direction, the project will leverage information from a circular manufacturing dataspace that will provide access to the datasets needed for multi-stage and multi-objective optimizations.

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