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Project cooperationUpdated on 25 June 2025

Circular quality assurance and assessment of remanufacturing

Chris Schönekehs

Research Associate at WZL RWTH Aachen University

Aachen, Germany

About

  • Utilize usage data with the help of AI-methods to increase product quality, reduce waste and extent service life

  • Implementing function-based quality inspections

  • Evaluating the condition of end-of-life products after a usage phase based on data in the digital shadow

  • 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

WZL RWTH Aachen University

University

Aachen, Germany

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    Innovative Advanced Materials (IAMs) for robust, fast curing sealants and coatings for manufacturing

    Hakan Bozcu

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    Bilecik, Türkiye

  • Project cooperation

    Digital ecosystem for circular value creation

    • Early idea
    • Consortium seeks Partners
    • Data technologies | Assistance and Expert systems
    • Data technologies | Design of an adaptable Digital Product Pass:
    • Data technologies | (AI based) process and system control technologies
    • Data technologies | (AI based) Material and Product Design, Decomposition and Separation
    • Data technologies | Approaches to support SME fully exploit the value of existing CVC-related data
    • Enabling technologies | (Advanced/smart) Sensors, e.g., enabling materials, components and product flows measurement
    • Data technologies | Interoperability of CVC-relevant data ecosystems, quality assurance and traceability across systems
    • Enabling technologies | Industry 4.0 technologies (IoT, big data analytics) for monitoring and managing circular value chains
    • 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 | 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.
    • Enabling technologies | Manufacturing and machine learning, e.g., to increase the flexibility of industrial processes, modular approaches, reduce use of materials, quality assurance and certification of products)

    Yoav Nahshon

    Team Leader Materials Informatics at Fraunhofer IWM

    Freiburg, Germany

  • Expertise

    Data-Driven Optimization for Circular Business Models

    • Data ecosystems
    • Industry 4.0 technologies
    • Manufacturing and machine learning
    • Network design of reverse supply chains
    • Simulation models and predictive analytics
    • Life cycle assessment / Product life cycle management

    Martin Absenger

    Managing Director at DIVU

    Graz, Austria