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.

A Digital Twin of a production plant comprises a variety of simulation models. When these models are coupled for co-simulation, they can reflect the behavior of the entire production plant and be used in various application scenarios. To leverage the benefits of the Digital Twin and simulation during the operational phase, manual efforts in model adaption must be automated. This contribution presents a flexible co-simulation approach that enables automated selection of simulation tool interfaces, parameterization, and execution of different model configurations. Finally, application scenarios in the context of Power-to-X production are discussed, and the current implementation of the approach is presented.

The rapid innovation in the automotive industry highlights the increasing importance of user comfort, especially when integrated with advanced learning scenarios. However, there is a noticeable gap in research focusing on vehicle cabin comfort, particularly in the context of learning and personalization of features. This study conducts a systematic literature review to assess the current state of research in this area. By utilizing federated learning with personalization, a novel and promising technique, various use cases related to vehicle interior comfort are explored. These use cases help derive the requirements needed to address the research question. The methodology of the systematic literature review is detailed, including the evaluation of specific prerequisites. The key finding reveals that no existing study meets all the predefined requirements, underscoring the need for further research in this domain.

Software-intensive systems emerge in a multitude of variations to meet diverse customer requirements. To develop such variant-rich software systems, software product line (SPL) Engineering has emerged as a key strategy for managing the variability. However, the adoption of SPLs is highly complex due to the diversity of feature model formats and specifications, and the complexity of implementing variability. Leveraging the capabilities of powerful large language models (LLMs) can facilitate the adoption of SPLs. Nonetheless, these LLMs often lack knowledge of the various specifications of potential feature models as well as efficient implementation of different variability mechanisms. To address these challenges, we propose a novel method based on retrieval-augmented generation. This method generates reusable artefacts and a corresponding feature mapping based on a given feature model, thereby aiding system engineers in adopting an SPL.

This paper presents a novel design of a multi-agent system framework that applies large language models (LLMs) to automate the parametrization of simulation models in digital twins. This framework features specialized LLM agents tasked with observing, reasoning, decision-making, and summarizing, enabling them to dynamically interact with digital twin simulations to explore parametrization possibilities and determine feasible parameter settings to achieve an obj ective. The proposed approach enhances the usability of simulation model by infusing it with knowledge heuristics from LLM and enables autonomous search for feasible parametrization to solve a user task. Furthermore, the system has the potential to increase user-friendliness and reduce the cognitive load on human users by assisting in complex decision-making processes. The effectiveness and functionality of the system are demonstrated through a case study, and the visualized demos and codes are available at a GitHub Repository: https://github.comlYuchenXia/LLMDrivenSimulation

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.

Industrial automation is becoming increasingly networked, intelligent and autonomous. Digital Twins, which serve as virtual representations, are a key technology in this context. The Digital Twin of a modular production system contains many different models that are mostly created for specific applications and fulfil different requirements. In particular, simulation models created in the development phase can be used in the operational phase for applications such as prediction or operation-parallel simulation. Due to the high heterogeneity of the model landscape in the context of a modular production system, the plant operator is faced with the challenge of adapting the models in order to ensure an application-oriented realism in the event of changes to the asset and its environment or the addition of applications. Therefore, this paper proposes an approach for the continuous model adaption in the Digital Twin of a modular production system during the operational phase. An agent-based implementation of the concept demonstrates the benefits of the approach for an operational phase application scenario.

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.

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.

Behavior models in the Digital Twin are relevant to many use cases such as digital product development, virtual system optimization or virtual commissioning. However, such behavior models are structured in the literature according to different criteria and levels, which complicates the selection of the correct properties for specific use cases. One criterion discussed very differently is the modeling depth of behavior models. This paper presents different approaches to structure behavior models from literature and introduces a new concept with focus on the modeling depth. These levels of modeling depths, as well as the characteristics of the different modeling depths, are illustrated using a representative industry scenario.