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Comprehensive machine data acquisition through intelligent parameter identification and assignment

Gönnheimer, Philipp; Karle, Andreas; Mohr, Lorenz; Fleischer, Jürgen


In today’s highly competitive manufacturing environment, process data monitoring continues to be of high priority, but often relies on modern communication interfaces being provided by PLC manufacturers. This paper proposes an alternative approach in which data is acquired automatically from various PLC models through available interfaces. Multiple Machine Learning algorithms are incorporated to identify machine parameters, which are then assigned to appropriate machine information models. All functionalities can be provided by a dedicated hardware module or as software modules on IPCs. The proposed approach can be integrated into existing Industry 4.0 efforts to accelerate digitalization in challenging environments.

Verlagsausgabe §
DOI: 10.5445/IR/1000141787
Veröffentlicht am 11.01.2022
DOI: 10.1016/j.procir.2021.11.121
Zitationen: 9
Zitationen: 10
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Produktionstechnik (WBK)
Publikationstyp Proceedingsbeitrag
Publikationsjahr 2021
Sprache Englisch
Identifikator ISSN: 2212-8271
KITopen-ID: 1000141787
Erschienen in 54th CIRP CMS 2021 - Towards Digitalized Manufacturing 4.0. Ed.: D. Mourtzis
Veranstaltung 54th CIRP Conference on Manufacturing Systems (CMS 2021), Online, 22.09.2021 – 24.09.2021
Verlag Elsevier
Seiten 720-725
Serie Procedia CIRP ; 104
Schlagwörter Digital Manufacturing System; Identification; Machine Tool
Nachgewiesen in Scopus
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