Abstract
The transition to Industry 4.0 requires the modernisation of legacy systems. This change poses challenges, in particular due to the different data formats and interfaces resulting from the heterogeneity of the components involved. To ensure seamless interoperability within Industry 4.0, a consistent data base is essential. The Asset Administration Shell (AAS), a standardised digital twin of assets, plays a key role in driving data-centric, interoperable solutions. The potential for efficient data integration means fewer errors and optimisation for the digitisation of legacy systems. This prompts an investigation into the necessary extensions and components within the AAS infrastructure to implement this process. Our paper outlines a strategy for integrating legacy systems into the AAS environment. We introduce new components that enable the modelling and embedding of information about assets throughout their lifecycle. The conceptualised architecture is designed to facilitate the assisted selection of relevant submodels, the reuse of existing resources and the automated generation of AASs. A service is proposed to verify the structure of the resulting AASs and the communication configuration. Using an articulated robot as a case study, we demonstrate the connection of data points via the OPC UA protocol. In addition, a prototype is presented that enables vertical data integration via the BaSyx DataBridge, highlighting the benefits of automated integration into AASs. The approach is versatile and is readily applicable to a variety of systems and scenarios as required.
Funded by the German Federal Ministry of Education and Research (BMBF).
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Zielstorff, A., Schöttke, D., Hohenhövel, A., Kämpfe, T., Schäfer, S., Schnicke, F. (2023). Harmonizing Heterogeneity: A Novel Architecture for Legacy System Integration with Digital Twins in Industry 4.0. In: Terzi, S., Madani, K., Gusikhin, O., Panetto, H. (eds) Innovative Intelligent Industrial Production and Logistics. IN4PL 2023. Communications in Computer and Information Science, vol 1886. Springer, Cham. https://doi.org/10.1007/978-3-031-49339-3_5
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