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Modelling Production System Families with AutomationML

  • Antonio Garmendia (Vortragende*r)

Aktivität: Vortrag oder PräsentationPosterpräsentationScience-to-science

Beschreibung

The description of families of production systems usually relies on the use of variability modelling. This aspect of modelling is gaining increasing interest with the emergence of Industry 4.0 to facilitate the product development as new requirements appear. As a consequence, there are several emerging modelling techniques able to apply variability in different domains. In this paper, we introduce an approach to establish product system families in AutomationML. Our approach is based on the definition of feature models describing the variability space, and on the assignment of presence conditions to AutomationML model elements. These conditions (de-)select the model elements depending on the chosen configuration. This way, it is possible to model a large set of model variants in a compact way using one single model. To realize our approach, we started from an existing EMF-based AutomationML workbench providing graphical modelling support. From these artifacts,we synthesized an extended graphical modelling editor with variability support, integrated with FeatureIDE. Furthermore, we validated our approach by creating and managing a production system family encompassing six scenarios of the Pick and Place Unit Industry 4.0 demonstrator.
Zeitraum10 Sep. 2020
Ereignistitel25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020, Vienna, Austria, September 8-11, 2020
VeranstaltungstypKonferenz
OrtÖsterreichAuf Karte anzeigen

Wissenschaftszweige

  • 202017 Embedded Systems
  • 102006 Computer Supported Cooperative Work (CSCW)
  • 201132 Computational Engineering
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  • 503015 Fachdidaktik Technische Wissenschaften
  • 502050 Wirtschaftsinformatik
  • 207409 Navigationssysteme
  • 102020 Medizinische Informatik
  • 102022 Softwareentwicklung
  • 102002 Augmented Reality
  • 201305 Verkehrstechnik
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  • 102040 Quantencomputing
  • 509026 Digitalisierungsforschung
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JKU-Schwerpunkte

  • Digital Transformation