InterQ is an European project. The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing process and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data.
25 European partners including research centres, industrial and technological companies, and universities are uniting their efforts. They are located in 11 countries and the project coordinator is Spain-based research centre IDEKO.
The project started on November 3rd, 2020, and will last 36 months. It has a total budget of 11 million euros.
Measuring and estimating manufacturing process variables
Measuring and estimating final product quality
Ensuring data reliability
Optimizing product quality for zero defect manufacturing
Ensuring traceability across the supply chain
InterQ will develop a platform based on five modules ready to increase the quality of European smart manufacturing. Those 5 InterQ modules will contribute to the creation, extension and usage of 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 techniques that automatize manual quality inspections to provide reliable data. The metrology based digital twin allows predicting the global production quality from a statistical sampling while 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 consistency and historical trend perspective and a repair system solves the detected error. 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 both 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 a 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 point of view.
Interlinked Process, Product and Data (PPD) quality framework.
InterQ software suite for quality control.
New generation of solutions: digital twins, AI systems and distributed ledger technologies.
InterQ concept will be applied in high-added value industrial applications.
Will represent the aerospace industry aiming at detecting any process deviation rapidly to avoid scrapping parts and allow direct reprocessing on the machine. For flight safety reasons, production data must be recorded for more than 20 years and distributed ledgers can be an option to increase trust between stakeholders.
Wind Power Energy
Will represent the energy sector focusing on the quality control of gears and on the interconnection of machine / process fingerprints with the final product quality. The trend to higher power windmills requires tighter tolerances for larger components.
Will represent the automotive industry with the manufacturing of electric motor blocks on which tool/process/machine parameters and part quality should be correlated to virtually control 100% of the production. The product quality digital twin should also reduce the ramp-up time for new productions.
Increased equipment productivity through rapid error localisation (10%).
Reduction of ramp-up time (>15%) using smart sensors/actuators and existing production data sets.
AI-driven instrumentation stimulating the transformation towards smart and fast processes leading to decreased time-to-market (time reduction >10%).
Significant increase in quality of manufactured products leading to a reduction of scrap of at least 50%.
PROJECT KEY FIGURES
11 million budget