Quality control in smart manufacturing (IA)

To address the challenge of data reliability, the sensors, actuators and instruments used at various levels of integration in the manufacturing process – often operating under adverse physical conditions – need to provide adequate levels of data accuracy and precision. Measurement traceability should ensure optimal manufacturing quality. Furthermore, suitable modelling and simulation approaches and data fusion techniques are needed to interpret and use sensor/actuator data in a factory.

Proposals should therefore address at least three of the following aspects:

• Integrate intelligent, cognitive, adaptive and cost-effective instruments and systems of sensors/actuators for process monitoring and control (e.g. virtual sensors and digital twins) into existing production or pilot lines;
• Showcase real-time data validation within an actual production line, and incorporate data integrity strategies based on, e.g. distributed ledger (blockchain) technology.
• Demonstrate how distributed, time stamped and persistent solutions for automated collection, storage, analysis and use of production data can lead to an integrated approach to zero-defect manufacturing;
• Develop strategies for rapid line qualification and reconfiguration based on large pre-existing data sets and related open protocols.

Certification, regulatory and standardisation activities related to the proposed solutions should be included in the proposal.

Proposals submitted under this topic should include a business case and exploitation strategy, as outlined in the Introduction to the LEIT part of this Work Programme.

Activities should start at TRL 5 and achieve TRL 7 at the end of the project.

The Commission considers that proposals requesting a contribution from the EU between EUR 8 and 10 million would allow this specific challenge to be addressed appropriately. Nonetheless, this does not preclude submission and selection of proposals requesting other amounts.

Smart factories are characterised by processes involving interlinked work pieces and associated tools as well as logistics operations. These are generating large amounts of data, which can be used for analysis and prediction as well as to optimise the quality of manufacturing operations and manufactured products. However, a major challenge for manufacturing is the reliability of data.

• 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%.

Relevant indicators and metrics, with baseline values, should be stated clearly in the proposal.