Research Group MiP | CiP
Research project

InterQ | Interlinked Process, Product and Data Quality framework for Zero-Defects Manufacturing

InterQ project wants to boost the quality of manufacturing operations and manufactured products linking the process, product, and data quality across the value chain. The project will employ a new generation of Digital Technologies and Artificial Intelligence-driven applications, including digital twins, virtual sensors, AI-driven decision support systems, data fusion and distributed ledger technology.

Coordinator: Nicolas Jourdan M. Sc. (CiP)
Project manager in the research group (MiP):
Beatriz Bretones Cassoli M. Sc.
Amina Ziegenbein M. Sc. M. Sc.
Duration: 003.11.2020 – 03.11.2023
Funded by: Horizon 2020
Website: https://interq-project.eu

Approach

InterQ will develop a platform based on five modules ready to increase the quality of European smart manufacturing. The 5 InterQ modules will create, extend, and use the PPD (Product, Process, Data) Hallmark to fulfil the specific project objectives.

InterQ-Process: monitors the quality of the manufacturing process with new physical sensors able to measure close to the cutting point and virtual sensors able to estimate the critical process characteristics. The machine/process fingerprint captures the normal production state to enable early detection of any deviation. The valuable information coming from the machine operator is also treated to allow further automatic data processing.

InterQ-Product: controls the quality of the product with new sensors and processing that automatize manual quality inspections to provide reliable data. The metrology based digital twin allows predicting the global production quality from a statistical sampling. Besides, the product quality prediction digital twin uses the process variables and measurements to estimate the product quality after each production step.

InterQ-Data: evaluates the quality of the data collected on the process and product, providing an industry 4.0 data quality service. Two verification layers detect anomalies on the gathered data from a consistency and historical trend perspective, and a repair system solves the detected error. Thus, it assures the reliability of the data transferred to optimization algorithms and reduces the mistakes on proposed optimization actions.

InterQ-ZeroDefect: improves the manufacturing quality using the reliable and meaningful data obtained from the process and the product. Virtual quality management allows assessing the product quality without requiring 100% metrology controls. AI-driven production optimization improves the process to control geometrical deviations, surface finish and surface integrity of the products.

InterQ-TrustedFramework: ensures complete product traceability using distributed ledgers trusted by all the parties. The supply chains actors can exchange product quality information using cryptographic protocols and trusted data sharing mechanisms provided by the InterQ-TrustedFramework. Moreover, it guarantees the complete integration of InterQ software and hardware from security, data management, and governance perspective.

The PTW of the TU Darmstadt works in the InterQ-Data and InterQ-ZeroDefect modules.

Expected Results

Expected results from the project include:

  • Development of interlinked Process, Product and Data (PPD) quality framework.
  • Development of a software suite for quality control.
  • Use of new generation of solutions: digital twins, artificial intelligence and distributed ledger technologies.

Website and additional information

For more information about the project and the latest updates, please visit InterQ, follow the project's Twitter or LinkedIn.

Acknowledgement

The InterQ project has received funding from the European Union's Horizon 2020 Research and Innovation. Programme under grant agreement No. 958357 and is an initiative of the Factories-of-the-Future (FoF) Public-Private Partnership.

Consortium

The consortium encompasses 25 partners from 11 countries. IDEKO, Spain, coordinates the project.