OPENZDM | Open Platform for Realising Zero Defects in Cyber-Physical Manufacturing

Summary

This project addresses the challenge of the manufacturing industry to deliver high-quality products at the necessary production rates while minimizing waste and energy consumption, maximizing efficiency and ROI. openZDM project is an Innovation Action that will develop and demonstrate in 5 representative production lines an open platform designed to realize ZDM. The platform integrates advanced ICT solutions & innovative non-destructive testing, setting the foundations for an innovative solution applicable to a large variety of mfg industries.

The 5 pilots represent the largest part of the EU's manufacturing sector, geographically (including plants in northern and southern areas of Europe), technologically (fully & semi automated, and manual processes) and from their value chain positioning (including Tier 1, Tier 2 suppliers, technology suppliers and OEMs). Furthermore, the choice of partners has been done considering strategic sectors for the green transition, in particular two energy intensive production processes (glass bottles, steel suspension arms), one process strategic for the electrification (production of batteries), one process consuming renewable materials (wood based panels) and one highly digitalized automotive assembly plant.

The project aims to develop a digital platform that builds on the state-of-the-art RAMI 4.0 and Asset Administration Shell (AAS) to implement intra-factory quality management practices, applicable to these different production environments. In addition several non-destructive inspection (NDI) methods and data-driven quality assessment techniques are considered for online defect identification and quality assessment, distributed at various stages along the manufacturing line. Finally, the Digital Twin and the related services is a key enabling technology for online process adaptation & prediction/prevention of defects, to achieve waste reduction and improved efficiency, aiming to significantly improve the production sustainability of CPPSs.

Attached files (1)
File Type
Pilots Poster SEPTEMBER update II.pdf PDF
More information & hyperlinks
Web resources: https://cordis.europa.eu/project/id/101058673
https://www.openzdm.eu/
Start date: 01-06-2022
End date: 30-11-2025
Total budget - Public funding: 10 875 885,00 Euro - 8 268 146,00 Euro
Twitter: @open_zdm
Cordis data

Original description

This project addresses the challenge of mfg industry to deliver high-quality products at the necessary production rates while minimizing waste and energy consumption, maximizing efficiency and ROI. openZDM project is an Innovation Action that will develop and demonstrate in 5 representative production lines an open platform designed to realize ZDM. The platform integrates advanced ICT solutions & innovative non-destructive testing, setting the foundations for an innovative solution applicable to a large variety of mfg industries.
The 5 pilots represent the largest part of the EU's manufacturing sector, geographically (including plants in northern and southern areas of Europe), technologically (fully & semi automated, and manual processes) and from their value chain positioning (including Tier 1, Tier 2 suppliers, technology suppliers and OEMs). Furthermore, the choice of partners has been done considering strategic sectors for the green transition, in particular two energy intensive production processes (glass bottles, steel suspension arms), one process strategic for the electrification (production of batteries), one process consuming renewable materials (wood based panels) and one highly digitalized automotive assembly plant. The project aims to develop a digital platform that builds on the state-of-the-art RAMI 4.0 and Asset Administration Shell (AAS) to implement intra-factory quality management practices, applicable to these different production environments. In addition several non-destructive inspection (NDI) methods and data-driven quality assessment techniques are considered for online defect identification and quality assessment, distributed at various stages along the manufacturing line. Finally, the Digital Twin and the related services is a key enabling technology for online process adaptation & prediction/prevention of defects, to achieve waste reduction and improved efficiency, aiming to significantly improve the production sustainability of CPPSs.

Status

SIGNED

Call topic

HORIZON-CL4-2021-TWIN-TRANSITION-01-02

Update Date

27-10-2022
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Factories of the Future Partnership - Made in Europe Partnership

Made in Europe (MiE)
HORIZON-CL4-2021-TWIN-TRANSITION-01
HORIZON-CL4-2021-TWIN-TRANSITION-01-02: Zero-defect manufacturing towards zero-waste (Made in Europe Partnership) (IA)

Project clusters are groups of projects that cooperate by organising events, generating joint papers, etc...

Zero-defect project cluster
C MANUFACTURING
C16 Manufacture of wood and of products of wood and cork, except furniture; manufacture of articles of straw and plaiting materials
C16.2 Manufacture of products of wood, cork, straw and plaiting materials
C23 Manufacture of other non-metallic mineral products
C23.1 Manufacture of glass and glass products
C23.13 Manufacture of hollow glass
C27 Manufacture of electrical equipment
C27.2 Manufacture of batteries and accumulators
C27.20 Manufacture of batteries and accumulators
C29 Manufacture of motor vehicles, trailers and semi-trailers
C29.2 Manufacture of bodies and coachwork for motor vehicles; manufacture of trailers and semi-trailers
C29.20 Manufacture of bodies and coachwork for motor vehicles; manufacture of trailers and semi-trailers
C29.3 Manufacture of motor vehicle parts and accessories
C29.32 Manufacture of other parts and accessories for motor vehicles
MiE KPI section - Impacts
Demonstrator showcasing digital platforms and engineering tools supporting creativity and productivity of R&D processes
Result items:

In the demonstrator of SONAE Arauco ES, the openZDM digital tools and platform will be used to achieve the following:- improvement of data collection and in-situ monitoring, namely in what concerns time-related annotation 
- improvement of inspection of quality issues through artificial intelligence and digital-twin technology.
- further advances in predictive quality based on data-driven and model-driven approaches for defect prediction and quality assessment.
- improvement in manufacturing decision making for ZDM strategies and process adaptation. This will also include recommendation models for new products, an explainable AI approach for a better understanding of Machine Learning decisions and a graphical user interface through UX/UI strategies.
 

thus effectively support productivity through the following aspects:

1. Improved decision-making at the plant level to reduce defects towards a zero defects paradigm

2. Improved product development (faster and with less waste generated)

3. Improved evaluation of machinery components 

MiE KPI section - Outcomes
Demonstrator showcasing an increased uptake of green manufacturing
Result items:

The vision of the future process with the contribution of the openZDM solutions targets an improved decision-making at the plant level to reduce defects towards a zero defects paradigm, that will facilitate defect prediction and recommendation of production recipes to adjust machine parameters according to predicted defects. In turn, improved products can be developed using the data-driven knowledge acquired, resulting in less wasted material. Towards this direction, the openZDM project performed an LCA at the beginning ot he project to compare it with the final one and also it supports integration through its platform to an LCA tool for on-demand LCA. 

Reduced scrap rate through zero defect and zero downtime manufacturing - demonstrator showcases reduction by 20%
Result items:

OPENZDM solutions for AI quality assessment and decision support aim for data driven defect prediction and estimation of machine components degradation through drifts in data distribution. The main goal is to predict defects such as broken paper, dust or glued paper so that changes in machinery/parameters can be performed and the defect avoided and also to estimate degradation based on slight and smooth changes in the process start occurring due to component wear-out / degradation and, if data is representative enough, it may be different data distributions in time.

Demonstrator showcasing artificial intelligence (AI) and data analytics tools’ uptake
Result items:

OPENZDM is expected to contribute to the SONAE Arauco use case in the aspects of proactive quality control towards zero-defect manufacturing through the digital twin enabled machine learning approaches for data analytics and quality assessment.

MiE_25-27_RP02: Excellent productive and flexible Manufacturing automation for open strategic autonomy
MiE_25-27_RP02_PastCallTopics(main)
HORIZON-CL4-2021-TWIN-TRANSITION-01-02: Zero-defect manufacturing towards zero-waste (Made in Europe Partnership) (IA)
MiE_25-27_RP03: Recovering and preserving the European leadership in strategic and high value added products
MiE_25-27_RP10: Data spaces and cloud/edge solutions for responsive and robust manufacturing
Specific Objective 1: Excellent, responsive and smart factories & supply chains
R&I Objective 1.3: Zero-defect and zero-downtime high-precision manufacturing, including predictive quality and non-destructive inspection methods
HORIZON-CL4-2021-TWIN-TRANSITION-01-02: Zero-defect manufacturing towards zero-waste (Made in Europe Partnership) (IA)
Specific Objective 2: Circular products & Climate-neutral manufacturing
R&I Objective 2.1: Ultra-efficient, low energy and carbon-neutral manufacturing
HORIZON-CL4-2021-TWIN-TRANSITION-01-02: Zero-defect manufacturing towards zero-waste (Made in Europe Partnership) (IA)
Horizon Europe
HORIZON.2 Global Challenges and European Industrial Competitiveness
HORIZON.2.4 Digital, Industry and Space
HORIZON.2.4.1 Manufacturing Technologies
HORIZON-CL4-2021-TWIN-TRANSITION-01
HORIZON-CL4-2021-TWIN-TRANSITION-01-02: Zero-defect manufacturing towards zero-waste (Made in Europe Partnership) (IA)