Köhler, Tim und Fraunholz, Thomas und Rall, Dennis und Larsen, Lars und Görick, Dominik und Schuster, Alfons (2024) RECAST: An Open Source Platform for Item-Specific Capturing of Real Production Processes. In: Proceedings of the 2024 8th International Conference on Cloud and Big Data Computing (ICCBDC) ICCBDC. Association for Computing Machinery. doi: 10.1145/3694860.3694861. ISBN 9798400717253.
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Offizielle URL: https://dl.acm.org/doi/10.1145/3694860.3694861
Kurzfassung
The digitization of production is an essential component of Industry 4.0. The collection of data in the production process is a decisive factor and a prerequisite for the use of artificial intelligence. At machine level, groundbreaking successes have been achieved in the field of predictive maintenance using machine learning, which is directly reflected in increased productivity and cost savings. In contrast, digitization at item level, i.e. the digital tracking of all items in the process context that ultimately lead to the finished product, is much more complex. A non-structured collection of data at item level prevents the use of artificial intelligence, for example in the quality testing of products. As part of the RECAST project, the extent to which existing non-networked production facilities can be digitized with this objective was investigated. As part of the work, a framework was created using exclusively open source components, which allows a digital representation of various real existing complex production processes in the aerospace industry to be created. With this digital twin, the system data can be recorded in real time on a item-specific basis. Thanks to the generic approach to process mapping, the results can be applied to a wide range of application fields in mechanical engineering and data-intensive administrative tasks.
elib-URL des Eintrags: | https://elib.dlr.de/208888/ | ||||||||||||||||||||||||||||
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Dokumentart: | Beitrag im Sammelband | ||||||||||||||||||||||||||||
Titel: | RECAST: An Open Source Platform for Item-Specific Capturing of Real Production Processes | ||||||||||||||||||||||||||||
Autoren: |
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Datum: | 2024 | ||||||||||||||||||||||||||||
Erschienen in: | Proceedings of the 2024 8th International Conference on Cloud and Big Data Computing (ICCBDC) | ||||||||||||||||||||||||||||
Referierte Publikation: | Ja | ||||||||||||||||||||||||||||
Open Access: | Nein | ||||||||||||||||||||||||||||
Gold Open Access: | Nein | ||||||||||||||||||||||||||||
In SCOPUS: | Nein | ||||||||||||||||||||||||||||
In ISI Web of Science: | Nein | ||||||||||||||||||||||||||||
DOI: | 10.1145/3694860.3694861 | ||||||||||||||||||||||||||||
Verlag: | Association for Computing Machinery | ||||||||||||||||||||||||||||
Name der Reihe: | ICCBDC | ||||||||||||||||||||||||||||
ISBN: | 9798400717253 | ||||||||||||||||||||||||||||
Status: | veröffentlicht | ||||||||||||||||||||||||||||
Stichwörter: | Cloud Computing, Big Data, Platform, Data-Storage | ||||||||||||||||||||||||||||
HGF - Forschungsbereich: | Luftfahrt, Raumfahrt und Verkehr | ||||||||||||||||||||||||||||
HGF - Programm: | Luftfahrt | ||||||||||||||||||||||||||||
HGF - Programmthema: | Komponenten und Systeme | ||||||||||||||||||||||||||||
DLR - Schwerpunkt: | Luftfahrt | ||||||||||||||||||||||||||||
DLR - Forschungsgebiet: | L CS - Komponenten und Systeme | ||||||||||||||||||||||||||||
DLR - Teilgebiet (Projekt, Vorhaben): | L - Produktionstechnologien | ||||||||||||||||||||||||||||
Standort: | Augsburg | ||||||||||||||||||||||||||||
Institute & Einrichtungen: | Institut für Bauweisen und Strukturtechnologie > Automation und Produktionstechnologie | ||||||||||||||||||||||||||||
Hinterlegt von: | Larsen, Lars-Christian | ||||||||||||||||||||||||||||
Hinterlegt am: | 09 Jan 2025 15:01 | ||||||||||||||||||||||||||||
Letzte Änderung: | 09 Jan 2025 15:01 |
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