Generative and Model-driven SDK development for the Industrie 4.0 Digital Twin

The concept of the Digital Twin (DT) and its implementation as Asset Administration Shell (AAS) is one of the key technologies for implementing Industry 4.0. By utilizing the AAS, use cases can be implemented with high interoperability. These use cases include optimizations and improvements of physical systems, like predictive maintenance or other artificial intelligence applications. For these use cases, historical data is key. However, there exists no guidance on how to handle historical data with the AAS. Furthermore, no integrated architecture exists that enables seamless storage and retrieval of historical data using the unified AAS meta-model and interface. Thus, we bridge this gap between use case and unified implementation by presenting multiple blueprints for data storage and retrieval motivated by use cases from Industry 4.0, healthcare, and civil construction. These data storage blueprints range from a mandatory change in the AAS infrastructure to augmentations of the existing AAS concepts. Furthermore, we showcase how the data model of the AAS can be utilized for a unified retrieval of historical data. In consequence, practitioners can quickly realize use cases that require historical data by tailoring and implementing the presented blueprints. Additionally, researchers can extend the presented guidance to further use cases, possibly from other domains.

Digital twins are a critical backbone technology for modern manufacturing and Industrie 4.0 (I4.0). The Asset Administration Shell (AAS) is becoming a popular foundation for modeling digital twins. Asset Administration Shells come in three successive levels of enhancement, also referred to as AAS (communication) types, though there is no common definition for these types. We conducted a systematic mapping study to find out how they are defined in the literature. As the analysis for type 2 AAS and type 3 AAS remains without direct results, we devised a novel classification schema from our findings via portfolio analysis. This schema can provide a foundation for the precise classification of AAS to guide researchers and practitioners in modeling digital twins.

Guidelines for providing digital twins

The wiring harness is one of the most expensive components of the automobile. It is still a custom-made product despite the high-volume production in the automotive industry. However, various trends, such as increasing fragility of supply chains and functional safety demands for autonomous driving are demanding the automation of the value creation processes. But in mass customisation, automation needs the digitalisation of all information flows. Machines and processes must be able to automatically gather all required information. In the wiring harness industry, much of the information has been geared toward processing by humans and has hardly been digitised at all. One of the foundations for Industry 4.0-oriented automation is ergo the existence of complete digital information of each wiring harness. A digital twin of the wiring harness is required. The Asset Administration Shell (AAS) is the technical concept for transparently interoperating with the digital twin. The project “Asset Administration Shell for the Wiring Harness” or in German (Verwaltungsschale für den Leitungssatz - VWS4LS) creates the first complete digital description of the wiring harness based on the AAS. As a result, all actors in the value network can access complete and consistent data (“single point of truth”), enrich it, and control its application through authorisations. To achieve this goal, a consortium from all levels of the value network was assembled for VWS4LS starting with the OEM, through the suppliers at the various levels of the value chain, and to the software suppliers and machine manufacturers. This publication presents the project and its initial results.

Software infrastructure for ready-to-use, data analytics-based Digital Twins utilising the Asset Administration Shell

Managing a product throughout its various lifecycle stages is essential for all stakeholders involved. Efficient management is supposed to streamline process, enhance product quality, increase customer satisfaction, and improve profitability. The current architecture faces challenges that impact trust among stakeholders because of its centralized architecture. This paper presents a blockchain-based solution to enhance trust and transparency in product lifecycle management. Using Asset Administration Shell (AAS) as a standardized digital representation, we model and share product digital twins via IPFS. The proposed architecture demonstrates how smart contracts and IPFS enable the creation, sharing, and traceability of digital twins throughout the product lifecycle, fostering collaboration among stakeholders. This is illustrated through the use case of an electric vehicle, where digital twins are created for critical components like the EV battery and tire and enabling tracking across different lifecycle stages. Additionally, we evaluate the implementation for scalability, latency, and cost efficiency.

Major benefit of Digital Twins lies in collaboration across value added chains based on semantic interoperability. The concept of the Asset Administration Shell (AAS) enables sharing of semantically enriched information in Industrie 4.0 application scenarios and beyond. Nevertheless, a technical infrastructure is needed that can ensure data sovereignty, i.e. integrity, confidentiality and availability of information shared across business partners in value added chains. This paper presents the current state of work on such a technical infrastructure for Asset Administration Shells (AAS). It combines three technologies: a blockchain books who changed an AAS and when, a peer-to-peer (P2P) distributed file system encrypts and stores each version of an AAS and a version-control system tracks changes to an AAS in detail. An exemplary value added chain is defined, which will be implemented in a demonstrator. This demonstrator will be used to evaluate and improve the proposed technical infrastructure for collaboration of AAS across value added chains.

The shift towards product personalization is the main enabler behind Industry 4.0 and drives the need to deploy flexible and reconfigurable assembly systems. This paper investigates the use of Digital Twins (DT) for enabling system flexibility and reconfigurability through various functionalities, leading to an overall resilience in the system. The suggested DT infrastructure involves two main components: a) Virtual representation of the shop floor, combining multiple sensor data and CAD models and b) Real-time multi-robot behaviour control. The communication layer between the physical and the virtual agents in the level of Digital Twin is realised through the ROS framework. The integration of the DT in an overall production system with various components (like planning systems) is realised through the Asset Administration Shell (AAS) and custom ROS-AAS connectors responsible for translating data between the two formats. Finally, an orchestration system (referred as Station Controller) that is responsible for dispatching planned tasks to the assigned resources has been implemented. The developed infrastructure is validated through a case study inspired by the consumer electronics industry focusing on the assembly of two different products, a trimmer head, and a monitor cover. Nevertheless, the proposed solution has been designed to be easily applicable to any similar scenario.

Objective: The article aims to develop a flexible architecture for network enterprises based on multi-agent technology. The goal is to enable dynamic configuration of value chains involving multiple business partners and to provide an adaptable framework for innovative product creation through the use of digital twins and digital threads. Methods: The research employs the RAMI (Reference Architectural Model for Industrie 4.0) framework to design the architecture of network enterprises. Multi-agent technology is used to ensure the integration of business processes at different levels of management, combining horizontal and vertical integration. The implementation of cyber-physical systems through digital twins and the Asset Administration Shell is central to this approach. Results: The proposed architecture demonstrates the ability to flexibly configure production processes, adapting to changes in market needs and technical constraints. The use of multi-agent technology enhances the adaptability, reliability, and scalability of the enterprise, ensuring seamless communication between digital and physical systems. Contribution: This research contributes by advancing the use of multi-agent technology in network enterprises, highlighting its role in the digital transformation of production. The integration of digital twins and the RAMI framework enables greater efficiency in managing value chains, improving the response time to dynamic changes and ensuring higher flexibility in business operations. Conclusions: The study concludes that the combination of multi-agent technology and the RAMI framework provides a robust architecture for network enterprises. This approach allows for the rapid reconfiguration of production processes and enhances the overall efficiency and adaptability of enterprises in competitive market environments. Future research is suggested to explore further operational scenarios and ensure functional suitability and security of the system.

The factory operators lack 3GPP network knowledge and there is a requirement for industrial 5G networks to enable zero-touch automation which will translate factory business expectations to technical expectations. Integrating the intent-based operations is a key approach to implement intelligent network management system which can automatically verify, deploy, configure and optimize itself to meet the targets and goals. However, there is still a need for human intervention (e.g., factory operators) to place the intents. In this paper, we propose a solution based on Asset Administration Shell (AAS) to implement digital twins of the 5G network and 5G-capable factory devices. The proposed solution approach implements necessary submodels in AAS instances, so that the factory devices can place their own intents with minimized manual intervention. This framework is implemented and demonstrated with an automation system in conjunction with a 5G emulation environment.

Energy management systems are an important tool for increasing the energy efficiency of buildings. However, the widespread availability of such systems is offset by the complexity and high costs of implementation, as well as a lack of data. These obstacles can be addressed by using standardized digital twins of technical components. In combination with homogeneous semantics and standardized interfaces as uniform access points to information, the implementation of an energy management system can be simplified. However, given the large number of different manufacturers of the technical components, an agreement on a common semantic standard of the information elements seems unlikely. Studies show that methods from the field of natural language processing can be used to process heterogeneous semantics. An agreement on a common vocabulary is no longer necessary. Instead, different semantics can be used and matched to a target vocabulary. The implementation of such a matching service reduces the configuration effort for the implementation of an energy management system. Providing an Industrie 4.0 interface that contains the semantic matching service within the components allows the interaction between them and an energy management system. A prerequisite for automated interaction is the availability of information in a uniform structure in the form of an information model. This paper presents the design of an Industrie 4.0 asset administration shell and initial submodels for an energy management system. Additionally, an Industrie 4.0 interface for the semantic matching service is implemented in a prototypical application of an energy management system. This implementation demonstrates how the configuration effort of such a system can be reduced by semantic matching.

Biopharma Is Going Digital … Bit by Bit

Dynamic simulation models are widely utilized to evaluate complex technical components and systems like electric drives or machines. They can support the development process of a production machine by avoiding an inadequate layout of components or an erroneous control design. However, the effort for building them is often too high for this purpose (lot size one). An automated model generation can be utilized to overcome the gap between efforts and advantages of dynamic simulations. This contribution presents an approach for simplifying the dynamic model generation of production machines by using the so-called Asset Administration Shell defined by the initiative Platform Industrie 4.0. The Asset Administration Shell was developed to aggregate all data necessary for maintaining the product across its life cycle. This includes component data and models as well as structural information about a machine. The generation process is performed by using the common FMI standard and a two-step procedure which allows the linkage of different simulation tools. The model generation is demonstrated by an example layout of a machine's internal direct current grid.

Industry 4.0 presents considerable challenges in data unification. The Asset Administration Shell is a key concept introduced by Reference Architecture Model Industry 4.0 to enable the storage and communication of data between assets. The current conceptualization of the Asset Administration Shell exhibits limitations when it comes to representing digital data. It lacks an effective methodology for categorizing and defining existing data as an asset and this can potentially hinder the full utilization of digital data as an integral asset within the Industry 4.0 infrastructure. This research proposes an asset model inspired from Asset Administration Shell to better represent the ever-evolving digital data as a valuable asset within the Industry 4.0 framework.

Asset Administration Shell - manufacturing processes energy optimization