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

Summary

The concepts of InterQ will be applied in three high-added value industrial applications.

The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing processes and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data.

The project develops five modules:

Thanks to the InterQ PPD quality hallmark, the quality of the process, product and data are interlinked, integrated and time stamped. A hallmark will be created after each stage, and the quality will be traced across the supply chain. A trusted framework will be implemented using distributed ledger (InterQ-TrustedFramework module) to exchange quality information.
The InterQ-Process module of the project will obtain more meaningful process data for quality optimization. This data will be obtained using new sensors close to the tool and by AI-driven virtual sensors.
The project presents new solutions (InterQ-Product module) to predict the final quality of the processes using digital twins fed by experimental data and new digital sensors to measure the product quality.
The reliability of data will be checked in two layers: in real time and based on historical and statistical analysis of the data streams (InterQ-Data module).
Finally, InterQ-ZeroDefect module will use the reliable information about the process and product quality to improve the production for Zero-Defect-Manufacturing by means of AI-driven applications.

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Result items:

High Quality Grinding Solution

Historical & Statistical data filtering and analysis software

Integrated optical sensor for surface quality control

Sensorised tool holder for force measurement in grinding processes with high sensitive bending moment signals

More information & hyperlinks

Web resources:	https://cordis.europa.eu/project/id/958357 https://interq-project.eu/
Start date:	01-11-2020
End date:	31-10-2023
Total budget - Public funding:	10 925 700,00 Euro - 8 988 750,00 Euro
Twitter:	@interq_project

Cordis data

Original description

InterQ project proposes a new generation of digital solutions based on intelligent systems, hybrid digital twins and AI-driven optimization tools to assure the quality in smart factories in a holistic manner, including process, product and data (PPD quality). The broad vision of InterQ project will allow controlling the quality of a smart manufacturing environment in an end-to-end approach by means of a PPD quality hallmark stored in a distributed ledger. The concepts of InterQ will be applied in three high-added value industrial applications.
The main objective of InterQ project is to measure, predict and control the quality of the manufactured products, manufacturing processes and gathered data to assure Zero-Defect-Manufacturing by means of AI-driven tools powered with meaningful and reliable data. The project develops five modules:
1) Thanks to the InterQ PPD quality hallmark, the quality of the process, product and data are interlinked, integrated and time stamped. A hallmark will be created after each stage, and the quality will be traced across the supply chain. A trusted framework will be implemented using distributed ledger (InterQ-TrustedFramework module) to exchange quality information.
2) The InterQ-Process module of the project will obtain more meaningful process data for quality optimization. This data will be obtained using new sensors close to the tool and by AI-driven virtual sensors.
3) The project presents new solutions (InterQ-Product module) to predict the final quality of the processes using digital twins fed by experimental data and new digital sensors to measure the product quality.
4) The reliability of data will be checked in two layers: in real time and based on historical and statistical analysis of the data streams (InterQ-Data module).
5) Finally, InterQ-ZeroDefect module will use the reliable information about the process and product quality to improve the production for Zero-Defect-Manufacturing by means of AI-driven applications.