Project cooperationUpdated on 25 June 2025
Circular quality assurance and assessment of remanufacturing
About
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Utilize usage data with the help of AI-methods to increase product quality, reduce waste and extent service life
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Implementing function-based quality inspections
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Evaluating the condition of end-of-life products after a usage phase based on data in the digital shadow
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Assess the economic and environmental potential of Re-X-strategies while using the digital shadow for low risk implementation
Stage
- Early idea
Type
- Consortium seeks Partners
Organisation
Similar opportunities
Project cooperation
Innovative Advanced Materials (IAMs) for robust, fast curing sealants and coatings for manufacturing
Hakan Bozcu
Senior Innovation Projects Specialist at Eczacıbaşı Building Products
Bilecik, Türkiye
Project cooperation
- Already defined
- Consortium seeks Partners
- Data technologies | Design of an adaptable Digital Product Pass:
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Diego Galar
Professor at Luleå University of Technology-Division of Operation and Maintenance Engineering
Lulea, Sweden
Project cooperation
Digital Twins for Remanufacturing
- Already defined
- Consortium seeks Partners
- Enabling technologies | Robotic / handling - and assistance systems
- Data technologies | (AI based) process and system control technologies
- Data technologies | (AI based) Material and Product Design, Decomposition and Separation
- Data technologies | Algorithm that shows the (positive) impact of a Circular Economy process or Circular Economy product
- Enabling technologies | Life cycle assessment / Product life cycle management – e.g., Digital Twin / Digital Product Passport
- Data technologies | Simulation models and predictive analytics to assess the scalability of circular processes across industries
- Enabling technologies | Reverse Manufacturing (e.g. adaptive automation for high variance, sorting, sophisticated logistic systems)
- Enabling technologies | AI-driven diagnostic systems, e.g., for assessing the viability of reused, remanufactured, and recycled components
- Data technologies | (AI based) recognition systems (e.g. image recognition) to evaluate materials, components and products and determine the best use paths
- Data technologies | Data ecosystems for the realisation of circular value creation exploiting the full potential of digitalisation – e.g., harnessing existing, purpose-built platform solutions.
Adrian Barwasser
Research Fellow at Fraunhofer IAO
Stuttgart, Germany