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Report

Teaser, summary, work performed and final results

Periodic Reporting for period 1 - Z-Fact0r (Zero-defect manufacturing strategies towards on-line production management for European factories)

Teaser

Summary

Manufacturing represents approximately 21 % of the EUâ€™s GDP and 20 % of its employment, providing more than 30 million jobs in 230 000 enterprises, mostly SMEs. Nowadays, the efficiency and sustainability of the manufacturing processes of high-tech products depend on the introduction of Advance Manufacturing Technologies in the production processes. The development of metrology solutions for zero defect applications is considered as a robust technology able to provide a vast competitive advantage to manufacturing companies.
The z-fact0r solution comprises five modules that are to be used in multi stage production systems with the following targets:
i. The early detection of the defect (Z-Detect)
ii. The prediction of defect generation (Z-Predict)
iii. The prevention of defect generation by recalibrating the production line (multi stage), as well as defect propagation in later stages of the production (Z-Prevent)
iv. The reworking of the product, using additive and subtractive manufacturing techniques (Z-Repair) and
v. The management of the aforementioned strategies through event modelling, KPI (key performance indicators) monitoring and real time decision support(Z-manage)

Figure 1-1

The Z-Fact0r is expected to have a strong social character in that it aims to achieve zero defect manufacturing, therefore contributing to:
i. Securing a qualitative and sustainable manufacturing industry by reducing the production cost -including the energy consumption- per good and by increasing customer satisfaction.
ii. Contributing to the reduction of unemployment in Europe, by helping European Industries become more competitive.
iii. Contributing to environment protection by reducing material and energy waste.

Work performed

Initially, the project website was launched at month M3 (D9.1) as a means for continuous dissemination and promotion of the project along with accounts in social media (twitter, LinkedIn, YouTube). Another deliverable that was completed early on (M2, D1.2) was the analysis of the current State of Art for the technologies and products that will be implemented in the project, as well as a trend and a SWOT analysis and a list of completed and ongoing similar projects.
During the first months of the project, all partners were highly involved in the preparatory phase (WP1) of defining the user requirements and the system architecture. System requirements (M3, D1.1) was led by DURIT and presents the feedback obtained from the end users and technology providing partners. The Volere methodology was implemented and together with interviews, a list of functional and non-functional system requirements were listed, resulting to 55 requirements. Next on, EPFL led the system design and architecture approach (M5, D1.3) where the technologies and software components of the partners were collected and categorized. Based on the standard ISO/IEC/IEEE 42010 (2011) â€œSystems and software engineering â€” Architecture descriptionâ€, an overall system architecture was designed including the main software functionalities, data information flows, middleware and other external sources, in such a way that all future efforts are coherently focused on the same, commonly shared and accepted objectives and functionalities are ensured. Following, INTERSEALS led the effort on documenting the Use Cases(UC) from each End User (M6, D1.4), where each end-user selected two UC that they deemed most suitable for the Z-fact0r approach.
WP2 is in progress with no submitted deliverables. A part of the work focuses on Z-Detect where we focus at the gathering of structural data with the use of laser scanning, from samples sent by the end users, to DATAPIXEL, and the development of machine learning algorithms from CERTH that will classify samples into categories. Z-Repair is under development with the collaboration of CETRI and SIR, as well as Z-Manage, led by EPFL, that is the main component of the Z-Fact0r framework.
In WP3, the data management of the framework is being developed with CERTH, DATAPIXEL, HOLONIX and EPFL as leader of the Tasks. First drafts of deliverables have been submitted (D3.1, D3.2, D3.5, and D3.6). DATAPIXEL conducted a study focusing on the detailed description of the most common standards, sensor requirements and the specification and architecture of the sensor network. HOLLONIX developed the â€œMultisensorial network managerâ€. Lastly, CERTH used state of the art data analytics algorithms that contain machine learning methods, capable of processing multimodal data sources, as is the case for the multisensorial network of the project. The work will conclude at M30.
BRUNEL leads the effort in WP4, and has developed the algorithms for event modelling in Z-Predict (D4.1, M12) where the installation of Event Modelling algorithm for Z-PREDICT consists of three major components. EPFL led the work on the validation and verification of KPIs (D4.2, M15) that serves as an input to the Event Modeller, KPI modeller and Green Scheduler. A state of the art analysis on key performance indicators for zero-defect manufacturing was conducted, a list of KPIs and standards was provided, and a preliminary list of end-user key parameters and indicators that cover quality aspects of the product for monitoring the manufacturing process was established.
WP5 has just started (M16), with no submitted deliverables.
WP7 activities are in progress, whereas no deliverable was concluded yet (due to M42).
As far as Dissemination and Exploitation goes, the first Interim Plan (PUDR) was submitted (D9.2, M12) by CETRI, detailing a combined strategy for IP, Exploitation and Dissemination of Project Results throughout the lifecycle of the project.

Final results

The key innovation (IN) points of the project can be summarized as:
IN1: Decision Support Systems (DSS): EPFL, ATLANTIS.
IN2: Early Malfunction Analysis using Time Series Trend Analysis Techniques: CERTH, EPFL.
IN3: Real-time optimization: ATLANTIS, CERTH and EPFL.
IN4: Laser Scanning Technologies and 3D Point-Cloud generation techniques to zero defects applications in short periods of time (DATAPIXEL).
IN5: Machine condition monitoring for predicting defects: ATLANTIS.
IN6: System Management and Optimisation Systems: UBRUN.
IN7: Discrete Event Modelling Systems (DES): UBRUN.
IN8: Deburring technique using robotic arms: SIR.
IN9: Production Management Module-ok-HOLONIX
IN10: Components repairing techniques combining additive manufacturing and laser processing: CETRI.

Z-Fact0r is expected to have impact in multiple stages of the manufacturing sector. The Z-Fact0r as a complete solution would be responsible for the reduction of the defected parts and also the reduction of the defected parts that are throw away for recycling. This will affect positively the total production cost and also the efficiency of the production system. Further to that by implementing such strategy will help manufacturers to provide highly customized products of high quality at reasonable prices. Further to that by utilizing the data from the prediction module will give to the manufacturers insights of their processes and therefore improve them and potentially eliminate defected parts, which has impact at the final cost of the product. Besides the cost impact, the above reasons will reduces also the material waste and the power needed for manufacturing of products. This will turn the production systems more eco-friendly something that is crucial nowadays.