A Container-based Approach For Proactive Asset Administration Shell Digital Twins

TL;DR

The paper addresses the limitation of static Asset Administration Shells by introducing a submodel-based architecture that embeds executable, containerized services within AAS packages. It presents a Service Execution Submodel, an event-driven system architecture, and a workflow for dynamic service activation, demonstrated through a CNC milling case study that decouples machine-specific behavior from portable control logic. Key contributions include standardized service metadata within AAS, runtime orchestration of containers, and a pathway toward AI-enabled, proactive digital twins. The work lays the foundation for reusable, interoperable value-added services in industrial DT ecosystems and outlines a roadmap for security, evaluation, and real-world deployment.

Abstract

In manufacturing, digital twins, realized as Asset Administration Shells (AAS), have emerged as a prevalent practice. These digital replicas, often utilized as structured repositories of asset-related data, facilitate interoperability across diverse systems. However, extant approaches treat the AAS as a static information model, lacking support for dynamic service integration and system adaptation. The existing body of literature has not yet thoroughly explored the potential for integrating executable behavior, particularly in the form of containerized services, into or from the AAS. This integration could serve to enable proactive functionality. In this paper, we propose a submodel-based architecture that introduces a structured service notion to the AAS, enabling services to dynamically interact with and adapt AAS instances at runtime. This concept is implemented through the extension of a submodel with behavioral definitions, resulting in a modular event-driven architecture capable of deploying containerized services based on embedded trigger conditions. The approach is illustrated through a case study on a 3-axis milling machine. Our contribution enables the AAS to serve not only as a passive digital representation but also as an active interface for executing added-value services.%, thereby laying the foundation for future AI-driven adaptation and system-level intelligence in digital twin environments.

Figures (5)

• Figure 1: The current state of the community effort for the development of AAS (left) vs. the aspired goal within this work (right), based on tao2018digital.
• Figure 2: Conceptual component diagram of the architecture, illustrating the primary components and their logical dependencies.
• Figure 3: Sequence diagram of the import and service activation workflow.
• Figure 4: Activity diagram of the conditional logic for processing execution and termination triggers.
• Figure 5: Constellation of three AAS: bottom left AAS of the physical machine containing the real values, bottom right AAS of the simulation model of the OSACA milling machine. On top a service that takes the values from the physical machine, uses a programmatic correlation in a Docker container and writes the normed value to the model AAS.