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Knowledge-based conversion of finite state machines in manufacturing automation

: Schneider, Georg Ferdinand; Peßler, Georg Ambrosius; Terkaj, Walter

Volltext urn:nbn:de:0011-n-5320349 (553 KByte PDF)
MD5 Fingerprint: 3479b9732ed876c07c74167bd4642c00
(CC) by-nc-nd
Erstellt am: 12.2.2019

Procedia manufacturing 28 (2019), S.189-194
ISSN: 2351-9789
International Conference on Changeable, Agile, Reconfigurable and Virtual Production (CARV) <7, 2018, Nantes>
Zeitschriftenaufsatz, Konferenzbeitrag, Elektronische Publikation
Fraunhofer IBP ()

More and more information and communication technologies originating from the web are introduced in industrial automation systems. The vision for future automation systems includes intelligent self-descriptive components, which exchange information and potentially reason by themselves through knowledge-assisted methods. Formal domain descriptions are required to enable this vision, including knowledge related to mechanical, electrical and control domains. This work focuses on formalizing knowledge of the automation and control domain and investigates how knowledge-based methods can support the automated conversion between different formalisms for modelling discrete behaviour in manufacturing automation: finite state machines. We detail our approach by deploying the presented method in a use case related to the automation of a pick and place unit available from the literature.