Improving the Engineering Process in the Automotive Field Through AutomationML

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10449)

Abstract

For a decade, it has been officially known that the most cost-intensive part of a body-building project is software engineering. The reason for this is the fact that in the engineering process many different types of information from their respective tool chains must come together and be combined. This situation is intensified by the heterogeneous engineering tool landscape that makes it difficult to reuse existing data and information from finished engineering steps without resorting to a paper interface. For this reason, many representatives of the automotive industry came together to solve these problems which resulted in the AutomationML format. AutomationML is an independent data format that allows bridging the gap between the various engineering fields and tool chains, thereby improving the overall process. The goal of this article is to provide an insight into the currently defined AutomationML standard and its possibilities.

Keywords

Production Transfer of knowledge and technology Digitalization Virtual commissioning AutomationML Workflow management 

Notes

Acknowledgements

This work was supported by the European Union through the FP7-PEOPLE-2013-IAPP AutoUniMo project “Automotive Production Engineering Unified Perspective based on Data Mining Methods and Virtual Factory Model” (Grant Agreement No: 612207) and research work financed from funds for science for years: 2016–2017 allocated to an international co-financed project.

References

  1. 1.
    Ovtcharova, J., Häfner, P., Häfner, V., Katicic, J., Vinke, C.: Innovation braucht resourceful humans Aufbruch in eine neue Arbeitskultur durch virtual engineering. In: Botthof, A., Hartmann, E.A. (eds.) Zukunft der Arbeit in Industrie 4.0, pp. 111–124. Springer, Heidelberg (2015). doi: 10.1007/978-3-662-45915-7_12CrossRefGoogle Scholar
  2. 2.
    Vogel-Heuser, B.: Herausforderungen und Anforderungen aus Sicht der IT und der Automatisierungstechnik. In: Bauernhansl, T., ten Hompel, M., Vogel-Heuser, B. (eds.) Industrie 4.0 in Produktion, Automatisierung und Logistik, pp. 37–48. Springer, Wiesbaden (2014). doi: 10.1007/978-3-658-04682-8_2CrossRefGoogle Scholar
  3. 3.
    Hirzle, A.: AutomationML—ein Überblick. In: AutomationML—Fachexperten erklären das Format. SPS Magazin: Zeitschrift für Automatisierungstechnik, Marburg (2014). https://www.automationml.org/o.red/uploads/dateien/1391503893-SPS-Magazin_Whitepaper_AutomationML.pdf
  4. 4.
    AutomationML e.V.: AutomationML whitepaper and eCl@ss integration (2015). https://www.automationml.org. Accessed 10 June 2017
  5. 5.
    AutomationML e.V.: AutomationML whitepaper: OPC Unified architecture information model for AutomationML (2016). https://www.automationml.org. Accessed 10 June 2017
  6. 6.
    Drath, R., Barth, M.: Wie der Umgang mit unterschiedlichen Datenmodellen beim Datenaustausch im heterogenen Werkzeugumfeld gelingt. In: VDI-Berichte 2209, Tagungsband zur Automation 2013, Langfassung auf Tagungs-CD (12 Seiten), pp 339–344. VDI-Verlag, Baden Baden (2013)Google Scholar
  7. 7.
    IEC 62714-1: Engineering data exchange format for use in industrial automation systems engineering—automation markup language—part 1: architecture and general requirements (2014). IEC: www.iec.ch
  8. 8.
    IEC 62714-2: Engineering data exchange format for use in industrial automation systems engineering—automation markup language—part 2: role class libraries (2015). IEC: www.iec.ch
  9. 9.
    Drath, R., Schleipen, M.: Das CAEX-Rollenkonzept. In: Drath, R. (ed.) Datenaustausch in der Anlagenplanung mit AutomationML: Integration von CAEX, PLCopen XML und COLLADA, pp. 51–53. Springer, Heidelberg (2010)Google Scholar
  10. 10.
    AutomationML e.V.: https://www.automationml.org/o.red.c/projects.html. Accessed 10 June 2017
  11. 11.
    Hämmerle, H., Strahilov, A., Drath, R.: AutomationML im Praxiseinsatz: Erfahrungen bei der virtuellen Inbetriebnahme. In: atpedition, pp. 52–64. DIV Deutscher Industrieverlag GmbH/Vulkan-Verlag GmbH, München/Essen (2016)Google Scholar
  12. 12.
    IEC 62714-3: Engineering data exchange format for use in industrial automation systems engineering—automation markup language—part 3: geometry and kinematics (2017). IEC: www.iec.ch
  13. 13.
    AutomationML e.V.: Whitepaper AutomationML part 4: AutomationML Logic (2017). https://www.automationml.org/o.red.c/dateien.html?cat=3. Accessed 10 June 2017
  14. 14.
    AutomationML e.V.: Application recommendations: automation project configuration (2016). https://www.automationml.org/o.red.c/dateien.html?cat=3. Accessed 10 June 2017
  15. 15.
    AutomationML e.V.: AutomationML whitepaper communication (2014). https://www.automationml.org/o.red.c/dateien.html?cat=3. Accessed 10 June 2017
  16. 16.
    Lüder, A., Schmidt, N.: AutomationML—Erreichtes und Zukünftiges. In: AutomationML – Fachexperten erklären das Format. SPS Magazin: Zeitschrift für Automatisierungstechnik, Marburg (2014). https://www.automationml.org/o.red/uploads/dateien/1392190786-AutomationML_Artikel13_2014_SPS-Magazin.pdf

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Technische Hochschule IngolstadtZentrum für Angewandte ForschungIngolstadtGermany

Personalised recommendations