Modelling Production System Families with AutomationML

  • Antonio Garmendia (Speaker)

Activity: Talk or presentationContributed talkscience-to-science

Description

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.
Period10 Sept 2020
Event title25th IEEE International Conference on Emerging Technologies and Factory Automation, ETFA 2020, Vienna, Austria, September 8-11, 2020
Event typeConference
LocationAustriaShow on map

Fields of science

  • 202017 Embedded systems
  • 102006 Computer supported cooperative work (CSCW)
  • 202005 Computer architecture
  • 201132 Computational engineering
  • 102 Computer Sciences
  • 502032 Quality management
  • 502050 Business informatics
  • 207409 Navigation systems
  • 102020 Medical informatics
  • 102022 Software development
  • 102002 Augmented reality
  • 201305 Traffic engineering
  • 102034 Cyber-physical systems
  • 102015 Information systems

JKU Focus areas

  • Digital Transformation