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An AutomationML extension towards interoperability of 3D virtual commissioning software applications.
- Source :
- International Journal of Computer Integrated Manufacturing; Oct/Nov2024, Vol. 37 Issue 10/11, p1194-1213, 20p
- Publication Year :
- 2024
-
Abstract
- To achieve interoperability between different 3D virtual commissioning software, a generic virtual commissioning data model is required. AutomationML is a standard neutral format for interoperability in the engineering phase. However, the current AutomationML standard is not sufficient for full-scope 3D-based virtual commissioning data exchange, as attributes and modeling method of 3D virtual commissioning-related sensors, actuators and signal connections are not standardized in AutomationML. To fill this gap, the authors suggest extending AutomationML for interoperability in 3D virtual commissioning. In this paper, a case-driven iterative approach is introduced to evolve towards an AutomationML extension. This extension is gradually developed by taking the union of all virtual commissioning-related functions and attributes of 3D virtual commissioning software. During the iteration, naming rules are applied when a new attribute is added to the extension. With this approach, an initial AutomationML extension is created by implementing a first iteration. The interoperability performance of this extension is subsequently evaluated by conducting data exchange of a representative set of 3D emulation models between two 3D virtual commissioning software, namely Siemens NX and Visual Components, via self-developed 'Import' and 'Export' plug-ins. It shows that AutomationML extension-based data exchange converts 70% more attributes than that only based on AutomationML. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 0951192X
- Volume :
- 37
- Issue :
- 10/11
- Database :
- Complementary Index
- Journal :
- International Journal of Computer Integrated Manufacturing
- Publication Type :
- Academic Journal
- Accession number :
- 179968034
- Full Text :
- https://doi.org/10.1080/0951192X.2023.2294443