Periodic Reporting for period 2 - InterQ (Interlinked Process, Product and Data Quality framework for Zero-Defects Manufacturing)
Periodo di rendicontazione: 2022-05-01 al 2023-10-31
In existing factories, a substantial amount of data is generated from machine sensors, yet it lacks direct utility for improving products due to issues such as data volume, lack of product quality information, unreliable data, and limited data exchange in the supply chain. The InterQ project aims to achieve zero-defect manufacturing by utilizing AI-driven tools with meaningful and reliable data. It seeks to enhance manufacturing quality by integrating process, product, and data quality across the supply chain, breaking down data silos using distributed ledger technologies.
InterQ employs both direct and indirect quality measurements to optimize product quality. Virtual sensors and digital twins, along with new sensors measuring process parameters, contribute to predicting and controlling product quality. Emphasis is placed on continuous verification of data reliability in real-time and over the long term.
Aligned with European societal challenges, InterQ addresses issues such as job security, talent acquisition, knowledge-intensive job creation, environmental concerns, resource efficiency, and raw materials. The project has made significant contributions to the European manufacturing industry, improving equipment productivity, reducing ramp-up time, and enhancing product quality through the development of physical/virtual sensors and digital twins. This transformation towards smarter processes has led to decreased time-to-market and substantial reductions in scrap.
InterQ employs both direct and indirect quality measurements to optimize product quality. Virtual sensors and digital twins, along with new sensors measuring process parameters, contribute to predicting and controlling product quality. Emphasis is placed on continuous verification of data reliability in real-time and over the long term.
Aligned with European societal challenges, InterQ addresses issues such as job security, talent acquisition, knowledge-intensive job creation, environmental concerns, resource efficiency, and raw materials. The project has made significant contributions to the European manufacturing industry, improving equipment productivity, reducing ramp-up time, and enhancing product quality through the development of physical/virtual sensors and digital twins. This transformation towards smarter processes has led to decreased time-to-market and substantial reductions in scrap.
InterQ proposes five interconnected modules to enhance quality in Zero Defects Manufacturing. These modules contribute to the PPD Hallmark, a unit characterizing the quality of operations by integrating process, product, and data quality.
For process monitoring (InterQ-Process), focus lies on measuring parameters close to the TCP, introducing new sensors and actuators. Eight machine and three process fingerprints contribute significantly.
InterQ-Product aims to measure product quality using new sensors and digital twins, validated through various NDT technologies. InterQ-Data ensures data quality through edge-level verification and historical data validation, utilizing Erdre for data repair. DQaaS employs machine learning for quality checks.
The Zero Defects goal optimizes product quality with AI for ZDM, delivering solutions at the process level. The Trusted Framework is deployed on Hyperledger Fabric, allowing PPD Hallmark sharing in the supply chain.
InterQ is validated through industrial pilot cases in aerospace, wind power, and automotive sectors, leading to cost savings and process optimization.
Exploitation involves 22 individual results grouped into joint results, identifying a market opportunity for "High-quality grinding solutions."
Dissemination includes 17 publications, significant industry participation, and active involvement in the 4ZDM cluster and DMP-ZDM community for common dissemination activities.
For process monitoring (InterQ-Process), focus lies on measuring parameters close to the TCP, introducing new sensors and actuators. Eight machine and three process fingerprints contribute significantly.
InterQ-Product aims to measure product quality using new sensors and digital twins, validated through various NDT technologies. InterQ-Data ensures data quality through edge-level verification and historical data validation, utilizing Erdre for data repair. DQaaS employs machine learning for quality checks.
The Zero Defects goal optimizes product quality with AI for ZDM, delivering solutions at the process level. The Trusted Framework is deployed on Hyperledger Fabric, allowing PPD Hallmark sharing in the supply chain.
InterQ is validated through industrial pilot cases in aerospace, wind power, and automotive sectors, leading to cost savings and process optimization.
Exploitation involves 22 individual results grouped into joint results, identifying a market opportunity for "High-quality grinding solutions."
Dissemination includes 17 publications, significant industry participation, and active involvement in the 4ZDM cluster and DMP-ZDM community for common dissemination activities.
Main innovations in InterQ include:
Development of sensors and actuators near the tool center point, along with virtual sensors.
Creation of six inspection systems providing crucial product quality information.
Implementation of digital twins for predicting product quality, enabling closed-loop feedback control.
Novel application for online quality checking of sensor-provided data.
Erdre, a machine learning pipeline for erroneous data repair, surpassing the state of the art.
Milestone application of blockchain technology in industrial quality management.
Project output condensed into five modules with new sensors and quality control applications:
InterQ-Process: Process monitoring with mechatronic devices, new sensors, and AI-driven virtual sensors.
InterQ-Product: Digitizing and automating data collection for final part quality using new sensors and AI-driven digital twins.
InterQ-Data: Measures data quality in motion and over time through edge-level and historical pattern analysis.
InterQ-ZeroDefect: Provides a Quality hallmark dashboard, process quality optimization tools, and AI-driven applications for production optimization.
InterQ-TrustedFramework: Offers a data sharing platform based on distributed ledger technologies for managing PPD Quality hallmark in the supply chain.
Expected impact on production lines:
Increased equipment productivity through new sensors for quality measurement in process.
Reduced ramp-up time using smart sensors/actuators and existing production data stored in the ledger.
AI-driven instrumentation promoting the shift towards smart and efficient processes, decreasing time-to-market.
Significant improvement in product quality leading to reduced scrap.
In the initial project phase, impact was primarily observed in SMEs and machine tool builders, focusing on specific system and technology development. While in the second reporting period, the project's potential impact extends to end users and the manufacturing community, with ongoing growth expectations.
For manufacturing companies, InterQ has facilitated digitalization improvements in production lines, enabling the collection of process data and integrating quality tools for error identification and waste reduction. New data centralization strategies and AI applications have advanced their digitalization strategies.
Machine tool builders have seen increased value in their machines with the inclusion of new sensors and devices. Collaborations within technology providers of InterQ have led to jointly exploitable outcomes, creating market opportunities and disruptive solutions.
Technology providers in mechatronics and optics have connected with flagship manufacturing companies through InterQ, developing sensory elements with economic impact in the next phase. The project has also introduced ICT and AI experts to the manufacturing world, significantly impacting high-tech SMEs in the manufacturing market.
In line with the societal challenges faced by Europe, InterQ tackles issues such as job security, talent acquisition, knowledge-intensive job creation, environmental concerns, resource efficiency, and raw materials. The project has significantly impacted the European manufacturing industry by enhancing equipment productivity, reducing ramp-up time, and improving product quality through the development of physical/virtual sensors and digital twins. This shift towards smarter processes has resulted in a reduced time-to-market and substantial reductions in scrap.
Development of sensors and actuators near the tool center point, along with virtual sensors.
Creation of six inspection systems providing crucial product quality information.
Implementation of digital twins for predicting product quality, enabling closed-loop feedback control.
Novel application for online quality checking of sensor-provided data.
Erdre, a machine learning pipeline for erroneous data repair, surpassing the state of the art.
Milestone application of blockchain technology in industrial quality management.
Project output condensed into five modules with new sensors and quality control applications:
InterQ-Process: Process monitoring with mechatronic devices, new sensors, and AI-driven virtual sensors.
InterQ-Product: Digitizing and automating data collection for final part quality using new sensors and AI-driven digital twins.
InterQ-Data: Measures data quality in motion and over time through edge-level and historical pattern analysis.
InterQ-ZeroDefect: Provides a Quality hallmark dashboard, process quality optimization tools, and AI-driven applications for production optimization.
InterQ-TrustedFramework: Offers a data sharing platform based on distributed ledger technologies for managing PPD Quality hallmark in the supply chain.
Expected impact on production lines:
Increased equipment productivity through new sensors for quality measurement in process.
Reduced ramp-up time using smart sensors/actuators and existing production data stored in the ledger.
AI-driven instrumentation promoting the shift towards smart and efficient processes, decreasing time-to-market.
Significant improvement in product quality leading to reduced scrap.
In the initial project phase, impact was primarily observed in SMEs and machine tool builders, focusing on specific system and technology development. While in the second reporting period, the project's potential impact extends to end users and the manufacturing community, with ongoing growth expectations.
For manufacturing companies, InterQ has facilitated digitalization improvements in production lines, enabling the collection of process data and integrating quality tools for error identification and waste reduction. New data centralization strategies and AI applications have advanced their digitalization strategies.
Machine tool builders have seen increased value in their machines with the inclusion of new sensors and devices. Collaborations within technology providers of InterQ have led to jointly exploitable outcomes, creating market opportunities and disruptive solutions.
Technology providers in mechatronics and optics have connected with flagship manufacturing companies through InterQ, developing sensory elements with economic impact in the next phase. The project has also introduced ICT and AI experts to the manufacturing world, significantly impacting high-tech SMEs in the manufacturing market.
In line with the societal challenges faced by Europe, InterQ tackles issues such as job security, talent acquisition, knowledge-intensive job creation, environmental concerns, resource efficiency, and raw materials. The project has significantly impacted the European manufacturing industry by enhancing equipment productivity, reducing ramp-up time, and improving product quality through the development of physical/virtual sensors and digital twins. This shift towards smarter processes has resulted in a reduced time-to-market and substantial reductions in scrap.