Abstract
The adoption of digital technologies in manufacturing enables intelligent dynamic control approaches, at the cost of increased design complexity. In this paper, ontologies and delta-lenses are exploited to enable multi-scale models of a manufacturing system to map digital models at different scales and let data flow according to the level of fidelity. A workflow is designed to assess the capability of models with a lower level of details to approximate the behaviour of the original system, through the application of a hybrid delta-lens. The approach is illustrated with a user case and applied to an industrial case, aiming at deciding the positions of sensors in an assembly line.
| Original language | English |
|---|---|
| Pages (from-to) | 361-364 |
| Number of pages | 4 |
| Journal | CIRP Annals |
| Volume | 70 |
| Issue number | 1 |
| Early online date | 9 Jun 2021 |
| DOIs | |
| Publication status | E-pub ahead of print - 9 Jun 2021 |
Bibliographical note
Funding Information:This paper demonstrated how ontology-based approaches coupled with delta-lenses support multi-scale modelling of manufacturing systems. Further development will address the definition of more complex and customised dput functions, as well as test the nesting of delta-lenses for more levels of aggregation. The Huddersfield team would like to acknowledge the funding support from the ESPRC: EP/S001328, EP/P006930/1 and EP/R024162/1
Publisher Copyright:
© 2021 The Authors
Keywords
- Delta-lenses
- Digital twin
- Ontology-based modelling