The production of automotive electronics for future mobility is based on automated lines that secure highest quality and output. For the production and assembly of respective PCBs an effective ZDM approach and availability is paramount. Key features, such as “product/component quality”, “cost control” and “time-to-market” for multi-millions of products outline the situation in the automotive industry at the brink of a new economic scenario and govern the global competitiveness.
As to Equipment Data Collection and Communication, it is obvious that:
(i) Sensing and combining data from a variety of sources is essential to enable a progressive approach. Setting a standard for data formats in shop floor communication, such as “Bluetooth 4.0 for Autonomous Quality” is crucial, aiming at achieving an independent format for machine communication;
(ii) Autonomous quality control loops, as well as recognition a management of unstructured data for several production lines all over Europe requires establishing an “EU-Cloud “for safe, secure and trustworthy data processing;
(iii) The adoption of Big Data technology in supervisory and strategic decision-making actions on production lines and the full supply grid are the gateway to immediate reaction to problems.
The pilot supports the realization of a ZDM Scenario for multi-stage production lines through introduction of deep analysis and decision-making control loops, for capturing, communicating, secure storing and visualizing real-time holistic data on products, material, equipment, environment, human actions; in relation to quality control and processes. Essential KPIs, such as FPY, OEE, MTBF/MTTR, PPM and Control loop time throughout the supply network will be fully taken into account.
Expected Outcomes:
- A common and holistic semantic model able to represent concepts at different stages of product lifecycle;
- Standard methods and tool to gather, store and share data;
- Trustworthy digital continuity and data management;
- Flexible and user-friendly analytical tools - Human-centric big data visualization;
- Comprehensive way to share results among different management roles;
- Equipment and process simulation and optimization solution as major leverage towards the efficient realization of complex first-time-right parts;
- Implementation of a closed loop over the manufacturing process between digital and real domains.
| Country: | DE |
| Address: | Im Gewerbepark D19a, Regensburg 93059 |
Process Data from Manufacturing Data Lake is analysed online for suspicious outliers. A Webinterface enables online intervention by staff.
1. Augmented Reality is improving supporting processes Change over, Maintenance and Training. Partner PACE will apply their AR technology to avoid utilization of human resource in Maintenance documents handling. Instead Technologies like smart glasses and Holo lens will be applied. Virtual assistants will guide Maintenances staff through maintenance and repair processes instead. Same is targeted for Training.
2. Visualisation of machine and process data in realtime will enable immediate intervention in case of abnormal behaviour.
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| Continental Automotive GmbH
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| Institute for Applied Systems Technology Bremen GmbH (ATB)
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| ATOS SPAIN SA
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| IWF Technische Universitat Braunschweig
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| FRAUNHOFER GESELLSCHAFT ZUR FOERDERUNG DER ANGEWANDTEN FORSCHUNG E.V.
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| PACE Aerospace Engineering and Information Technology GmbH (PACE)
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| SINTEF MANUFACTURING AS (SINTEF)
Analyses Stations are equiped with Semantik Search Technology in order to generate consistent Data between standard failure catalogues and Teammember entries into Analyses Client.