AML4DT: A Model-Driven Framework for Developing and Maintaining Digital Twins with AutomationML
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ETFA 2021- IEEE 26th International Conference on Emerging Technologies and Factory Automation, September 7-10, 2021, Vasteras, Sweden, virtual event
As technologies such as the Internet of Things (IoT) and Cyber-Physical Systems (CPS) are becoming ubiquitous, systems adopting these technologies are getting increasingly complex. Digital Twins (DTs) provide comprehensive views on such systems, the data they generate during runtime, as well as their usage and evolution over time. Setting up the required infrastructure to run a Digital Twin is still an ambitious task that involves significant upfront efforts from domain experts, although existing knowledge about the systems, such as engineering models, may be already available for reuse. To address this issue, we present AML4DT, a model-driven framework supporting the development and maintenance of Digital Twin infrastructures by employing AutomationML (AML) models. We automatically establish a connection between systems and their DTs based on dedicated DT models. These DT models are automatically derived from existing AutomationML models, which are produced in the engineering phases of a system. Additionally, to alleviate the maintenance of the DTs, AML4DT facilitates the synchronization of the AutomationML models with the DT infrastructure for several evolution cases. A case study shows the benefits of developing and maintaining DTs based on AutomationML models using the proposed AML4DT framework. For this particular study, the effort of performing the required tasks could be reduced by about 50%.