Abstract
Keeping factories running at any time is a critical task for every manufacturing enterprise. Optimizing the flows of goods and services inside and between factories is a challenge that attracts much attention in research and business. The idea to fully describe a factory in a digital form to improve decision making is called a virtual factory. While promising virtual factory frameworks have been proposed, their semantic models lack depth and suffer from limited expressiveness. We propose an enhanced semantic model of a factory, which enables views spanning from the high level of supply chains to the low level of machines on the shop floor. The model includes a mapping to relational production databases to support federated queries on different legacy systems in use. We evaluate the model in a production line use case, demonstrating that it can be used for typical factory tasks, such as assembly line identification or machine availability checks.
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Notes
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Prefixes are defined according to http://prefix.cc.
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The query is limited to sequences of up to 9 machines but may be extended.
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Acknowledgments
This work has been supported by the German Federal Ministry of Education and Research (BMBF) in the context of the projects LUCID (grant no. 01IS14019C), SDI-X (no. 01IS15035C) and Industrial Data Space (no. 01IS15054).
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Petersen, N., Galkin, M., Lange, C., Lohmann, S., Auer, S. (2016). Monitoring and Automating Factories Using Semantic Models. In: Li, YF., et al. Semantic Technology. JIST 2016. Lecture Notes in Computer Science(), vol 10055. Springer, Cham. https://doi.org/10.1007/978-3-319-50112-3_24
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DOI: https://doi.org/10.1007/978-3-319-50112-3_24
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