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Generalizability of an Identification Approach for Machine Control Signals in Brownfield Production Environments

Gönnheimer, Philipp 1; Ströbel, Robin ORCID iD icon 1; Dörflinger, Roman 1; Mattes, Marcel 1; Alexander, Philipp 1; Wuest, Thorsten; Fleischer, Jürgen 1
1 Institut für Produktionstechnik (WBK), Karlsruher Institut für Technologie (KIT)

Abstract:

Digital transformation has been a central aspect of optimizing processes in manufacturing companies for several years now. A basic prerequisite of successful transformation is the vertical integration of all machines and machine tools to capture data at all levels. This can create further applications that enable more sustainable and resource-saving processes. At the same time cost- and quality-optimizing analyses such as failure detection, predictive maintenance or general process optimization represent major incentives for companies. While the necessary interfaces are now integrated in state-of-the-art machine tools, companies with older legacy machines face the problem that no such interfaces are readily available. Brownfield machine tools feature outdated technology that does not allow direct networking connectivity without further effort. To participate in the technological progress, a system was developed that allows to extract machine control signals from machine tools and identify them automatically as time series. This is compatible with several communication protocols (e.g., OPC UA) to be as universally applicable as possible. ... mehr


Verlagsausgabe §
DOI: 10.5445/IR/1000168687
Veröffentlicht am 26.02.2024
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2023
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000168687
Erschienen in Procedia CIRP
Verlag Elsevier
Band 120
Seiten 649 – 654
Bemerkung zur Veröffentlichung Part of special issue: 56th CIRP International Conference on Manufacturing Systems 2023
Schlagwörter Artifical intelligence, Automation, Digital manufacturing system, Machine Tool, Industry 4.0
Nachgewiesen in Dimensions
Scopus
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