Offshore Power-to-X platforms enable flexible conversion of renewable energy, but place high demands on adaptive process control due to volatile operating conditions. To face this challenge, using Digital Twins in Power-to-X platforms is a promising approach. Comprehensive knowledge integration in Digital Twins requires the combination of heterogeneous models and a structured representation of model information. The proposed approach uses a standardized description of behavior models, semantic technologies and a graph-based model understanding to enable automatic adaption and selection of suitable models. It is implemented using a graph-based knowledge representation with Neo4j, automatic data extraction from Asset Administration Shells and port matching to ensure compatible model configurations.

In the automation industry, concepts such as Digital Twins, representing virtual counterparts of physical assets, enhance efficiency, sustainability, and economic viability by enabling e.g., optimizations, advanced monitoring, and predictive maintenance. To achieve this, Digital Twins rely on models (including simulation models), which in turn depend on data describing the current state of the physical asset. As connectivity increases, the available data extends beyond the data of the sensors of the asset itself. It extends to data from the surrounding environment and even beyond. This introduces new challenges for Digital Twins and their models in terms of data integration. In previous research it was shown that current implementations are not adequately prepared for this scenario. Currently, no analysis in the literature addresses how interaction within a connected environment alters the requirements for Digital Twins. Therefore, this paper explicitly identifies the emerging challenges associated with extending the information acquisition scope of Digital Twins beyond the immediate physical asset. This is accomplished through a systematic analysis of potential scenarios that consider the inclusion of external data. Our analysis reveals several new requirements, such as the need for an extended description of the model’s purpose and the specific requirements for the data input. Digital Twins must possess enhanced capabilities to identify appropriate data sources within a dynamic production environment, as well as methods to manage multiple, potentially redundant data streams effectively. Through this analysis, we aim to highlight potential research directions for Digital Twins in the automation industry and advocate for the integration of interconnectivity into Digital Twin systems.

Efficiency and flexibility are continual demands on future production systems, and modularization has emerged as a strategy to address these challenges. In the process industry, the Module Type Package (MTP) standard was introduced to simplify the integration and configuration of modular production systems. Transferring the MTP technology to other indus-trial domains is an active research field. This paper describes how the MTP can be used to integrate robotic systems into mod-ular manufacturing plants without compromising the MTP standard. This fills a gap in existing literature and provides promising scientific and commercial opportunities. The study presents a layered concept encompassing all necessary compo-nents for the integration of robotic systems, from the robot's control to the orchestration of the modular plant. A prototypical implementation demonstrates the feasibility of the proposed ap-proach, successfully performing a typical robotic task, a Pick-and-Place operation, using MTP-based services. The imple-mented services, though representing basic robot functionali-ties, lay the groundwork for more complex services or nested service structures. In conclusion, this research contributes to the understanding of the potential integration of robotic systems in MTP-based modular production environments, paving the way for more sophisticated applications, and addressing open questions for future exploration. The prototypical implementation is publicly available on GitHub.

Industrial automation is driven by trends such as autonomy, intelligence and networking. Increasing energy demands, scarce resources and shorter product lifecycles pose challenges to production systems such as interoperability, flexibility and extensibility. The concept of the Digital Twin, acting as a virtual representation of a production system, can address these challenges. This paper presents a concept that realizes a dynamic production scheduling as partial function of the Digital Twin using the Asset Administration Shell and a service-oriented architecture. The authors specifically address automated asset production scheduling and propose an MQTT broker with a Functionality Dictionary server. Through the Asset Administration Shell interface, the proposed production scheduler communicates with the proposed product calendar to generate a specialized production schedule for each product. The evaluation scenario in a cyber-physical laboratory shows the advantages of this concept.

In the era of Industry 4.0, data is becoming increasingly vital for future manufacturing and digital twin applications. Currently, primarily asset-created data is utilized, while the potential benefits of incorporating external data from connected production systems remain untapped. This underutilization is largely due to the complex and time-consuming nature of integrating external data. Automating this process could significantly improve efficiency and unlock valuable insights for more effective decision-making. In this paper, we present the requirements for a system aimed at automating the integration of external data, considering factors such as data heterogeneity and the need to support diverse communication protocols. We further introduce an assistant system that facilitates this automated integration, enhancing manufacturing processes and digital twin applications. Furthermore, we provide a prototypical implementation of the assistant system in the context of a collaborative robot utilizing information from its surroundings obtained through external camera and LiDAR imaging. By addressing the challenges of external data integration, this research contributes to the advancement of data-driven manufacturing and the optimization of digital twin technologies.

Ideally, modern modular production plants are controlled via services with uniform interfaces and state machines. But in detail, services are of diverse nature. Due to this diversity, orchestration systems face a challenge of correctly "understanding" the available services and recognizing how and when these services can be used. This paper identifies fundamental service properties, enabling the categorization of services. Furthermore, the authors propose to assign an information model containing the service properties to each service. Consequently, an orchestration system can independently recognize, interpret, and accordingly coordinate the information about service properties and variants; they become explorable. Orchestration systems are thus put in a position to deal explicitly with different services of diverse control methods on their own.

The Digital Twin is one of the future key technologies of digitization and Industry 4.0. Through coupling the vast amount of available static and dynamic data about a physical asset with intelligent software functions, it aims for simplifying the increasingly complex functions and interconnections of automation systems. This makes it necessary to provide the Digital Twin with updated and high-quality data about the physical asset. However, little attention is paid to data not generated by the asset itself but from external data sources. This is partly due to a lack of methodologies for identifying and evaluating external data relevant for a specific Digital Twin. The contribution of this work is a categorization of data for the Digital Twin and a methodology to support the identification of data from outside of the physical asset. The work-in-progress refers to the context of industrial automation and is to be validated in the future in laboratories containing discrete manufacturing facilities at Pforzheim University and the University of Stuttgart.

This contribution pursues the approach to extend real and operative plant components by virtual elements to improve efficiency, safety, and reliability in the automation engineering process. Although some research has been done in the field of mixing real and virtual representations of different plant components, the presented Virtual-In-The-Loop approach promises a wide range of new applications. To enable a systematic investigation of its possibilities and limitations, we present a first-time categorisation of the opportunities for mixing real and virtual plant components and interfaces. Furthermore, we propose a terminology that enables the unambiguous naming of the different forms of mixtures. The results are intended to serve as an orientation and classification guide for future work in the field of Virtual-In-The-Loop Engineering.