i4Q

Industrial Data Services for Quality Control in Smart Manufacturing

i4Q Project will create a framework consisting of sustainable IoT-based Reliable Industrial Data Services (RIDS), that will provide solutions for factories in handling large amounts of data. The management and analysis of data derived from the work flows of factories will assist in the prediction and optimization of manufacturing processes that subsequently leads to improved process and product quality. Cost-effective, smart and small size interconnected factory devices will be used for online monitoring and control of manufacturing and will extract huge amounts of relevant data. i4Q Solutions will employ state-of-the-art communication protocols, ensuring data accuracy, precision and integrity through distributed ledger technology. The main objectives of i4q project are: 1) to develop, design and build the i4q framework, 2) to test and validate the project’s solutions in 6 use cases in different types of manufacturers and industrial sectors and 3) to facilitate technology uptake by the i4Q start-up company that is being created and long-term adoption of the i4Q Solutions by the industry.

MKLab/M4D the coordinator, will be responsible for the strategic and operational coordination of the project. Moreover, M4D will lead the design and the development of the smart manufacturing monitoring and alerting component, and the data integration and fusion solutions.

PUBLICATIONS

  1. G. Apostolou, M. Ntemi, S. Paraschos, I. Gialampoukidis, A. Rizzi, SVrochidis, I. Kompatsiaris, "Novel Framework for Quality Control in Vibration Monitoring of CNC Machining"  Sensors 2024, 24, 307. https://doi.org/10.3390/s24010307 OPEN ACCESS
  2. A. Kyriakopoulos; Ilias Gialampoukidis; Stefanos Vrochidis; Ioannis Kompatsiaris. "Adaptive Quality Diagnosis Framework for Production Lines in a Smart Manufacturing Environment". Machines 2023, 11(4), 499; https://doi.org/10.3390/machines11040499.
  3. A. Tsanousa, E. Bektsis, C. Kyriakopoulos, A. González, U. Leturiondo, I. Gialampoukidis, A. Karakostas, S. Vrochidis, I. Kompatsiaris, "A review of multisensor data fusion solutions in smart manufacturing: systems and trends", Sensor data fusion analysis for broad applications, MDPI.
  4. A. Karakostas, G. Apostolou, R. Poler, B. Andrés, R. Trevino, A. M. Nowak-Meitinger, R. Jochem, S. Vrochidis, "How to Collect Factories’ Requirements for Smart Manufacturing in Context of Quality Control: the i4Q Approach", In Proceedings of the 29th IEEE International Requirements Engineering Conference, Notre Dame, South Bend, USA, 20-24 September 2021.
  5. R. Poler, A. Karakostas, S. Vrochidis, A. Marguglio, S. Gálvez-Settier, P. Figueiras, A. Gómez-González, B. Mandler, S. Wellsandt, R. Trevino, S. Bassoumi, C. Agostinho, "An IoT-based Reliable Industrial Data Services for Manufacturing Quality Control", In Proceedings of the 2021 IEEE International Conference on Engineering, Technology and Innovation, 21-23 June 2021. OPEN ACCESS
  6. A. Karakostas, F. Fraile, R. Poler, S. Vrochidis, "Industrial Data Services for Quality Control in Smart Manufacturing - the i4Q Framework", In Proceedings of the 2021 IEEE International Workshop on Metrology for Industry 4.0 and IoT, Virtual, 07-09 June 2021.