Leveraging ontologies and Asset Administration Shells for decision-support: A case study on production planning within the injection molding domain
A fundamental aspect of Industry 4.0 is interoperable asset-to-asset communication, essential for creating cross-company “lab of labs”. Such collaboration enables seamless data exchange across companies, streamlining manual processes like evaluating the capability of assets for specific manufacturing processes. While foundational technologies for asset interoperability exist, their integration and application in industrial contexts remain limited. Our research explores the integration of ontologies, which structure domain knowledge, and Asset Administration Shells (AAS), which represent assets in a standardized manner, to facilitate industrial interoperability. We have developed an architecture using an ontology-based graph database populated with AAS data, allowing automatic linking of AAS instances to corresponding class nodes. To demonstrate practical value, we have implemented this architecture using standardized software and tools, applying it to assess technical capabilities for a customer request in injection molding. Results confirm the potential for asset-to-asset communication in industry via graph databases, with benefits in flexible and scalable data management. However, limitations include unaddressed data safety and security concerns, as well as the need for updated database entries when AAS instances change. Additionally, challenges in scaling to integrate other domain ontologies should be tackled in future research. This work lays a foundation for advancing interoperable, cross-company data-sharing ecosystems. • Using ontologies for establishing a graph database for production planning. • Automatic population of this database with values from asset administration shells. • Possible due to the application of standardized properties from data dictionaries. • This work presents a case study for production planning in injection molding. • Result: A web-based decision-support system using asset administration shells.
Authors
- Christian Hopmann (ORCID: https://orcid.org/0000-0003-4343-1508)
- Patrick Sapel (ORCID: https://orcid.org/0000-0002-2838-3141)
- Anna Garoufali (ORCID: https://orcid.org/0009-0009-9238-2205)
Institutions
- RWTH Aachen University (DE)
Publication Details
- Journal
- Internet of Things
- Published
- 2025-09-27
- DOI
- https://doi.org/10.1016/j.iot.2025.101739
- Primary Topic
- Business Process Modeling and Analysis
- Type
- article
- Field-Weighted Citation Impact
- 0.00