Model quality assurance in digital twins via the asset administration shell framework

Guidelines for providing digital twins

Industrie 4.0 maps the digital twin as the Asset Administration Shell. To develop this digital twin, the Asset Administration Shell meta-model is used. A developer needs high-quality libraries in their programming languages to build the Asset Administration Shell in efficient and ergonomic way. The existing representations of the meta-model are inconvenient for moving from the specification to realizations in software development. This leads to many, often manual, re-implementations of the Asset Administration Shell in each programming language. We present an approach to automatically generate libraries based on the intermediate representation of the meta-model. The approach allows for practical and simple development process of Industrie 4.0 components, and scales across programming environments and future changes in the meta-model.

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.

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 hold immense promise in accelerating scientific discovery and revolutionizing industries. The use cases for digital twins are diverse and proliferating, with applications across multiple areas of science, technology, and society, and their potential is wide-reaching. Despite the growing use of quality assurance approaches, standards, and frameworks in digital twins, the promise of digital twin benefits remains more aspirational than reality. Consequently, the confidence level in the prediction of these models is questionable. There is a lack of guidance on establishing standardization and interoperability as a foundation for integration of platforms, systems, and stakeholders within the digital twin ecosystem. To address this gap, we propose guidance for standardized quality assurance of digital twins in simulating product lifecycles, aiming to enhance regulatory decision-making. This proposed guidance is a step towards development of framework for standardized quality assurance of digital twins.

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.

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.

Digital twin applications in radiology and radiotherapy: Applications, challenges, and future perspectives.

Use of multi-agent system for industrial production control

Today's manufacturing sector undergoes essential changes due to emerging technologies and sustainability-focused practices alongside innovative operational methods. Industries worldwide are undergoing a substantial revolution as they adopt Industry 4.0 principles which involve extensive digitization and the implementation of artificial intelligence (AI). Traditional production methods are experiencing substantial transformations as Industry 4.0 principles and advanced technologies become integrated to increase industrial efficiency. According to Nathany (2024), global manufacturing output could achieve up to 25% growth through the adoption of Industry 5.0 technologies which also promises major operational cost savings. This research investigates the central elements of Industry 4.0 which consist of additive manufacturing, big data analytics, digital twins, artificial intelligence and machine learning, the Internet of Things (IoT), cloud computing, augmented and virtual reality along with blockchain technology. This study analyzes the applications of these technologies as well as their benefits and how they affect manufacturing processes. The research focuses on the ways that these technologies boost efficiency while improving sustainability and competitiveness in manufacturing. Manufacturing has experienced a revolutionary transformation through the integration of artificial intelligence and IoT which enables advanced defect detection capabilities as well as predictive maintenance and real-time monitoring. The method boosts human-machine teamwork alongside operational effectiveness and quality assurance while supporting personalized manufacturing and enhanced environmental sustainability (Nathany, 2024). This paper examines how sustainability and decarbonization will become essential factors in manufacturing's future path. Reducing environmental impact alongside optimizing resource use represents critical elements of the manufacturing industry’s transition. Additive manufacturing stands at the forefront of manufacturing evolution due to its infinite design capabilities and its efficient use of materials. Additive manufacturing generates substantial economic and environmental benefits through waste reduction and production customization. The research investigates the application of big data analytics for predictive maintenance and real-time monitoring and explains how these technologies increase production efficiency while minimizing breakdowns and unscheduled downtime. Big data analytics applications result in a 25% decrease in breakdowns and unscheduled downtime within manufacturing sectors as shown by Wang & Wang (2017). The study investigates how digital twins function as virtual models of physical assets to reveal their transformative capabilities. Real-time data analytics enable digital twins to optimize performance while predicting issues and detecting problems which then lead to substantial cost savings and enhanced planning efficiency and productivity. The advancement of digital twin technology reflects ongoing enhancements in product design and industrial manufacturing operations. Research shows digital twins can achieve 30% savings in costs while also reducing planning time by 20% and achieving 8% productivity enhancements (Kim, 2017). The discussion covers how cloud computing delivers essential computational power to support AI applications. Cloud technologies enable seamless data sharing between systems while providing adaptable solutions and support for manufacturing services that operate on demand. This research looks at how augmented and virtual reality systems can help manufacturing become more efficient by improving operational procedures and training programs through simulation-based methods. The research considers how blockchain technology supports sustainable operations while ensuring secure and transparent manufacturing networks.The study concludes by proposing future research pathways in Industry 4.0 domains which cover organizational management comprehensively and incorporate the latest organizational models. Innovative technologies will continue to generate vast opportunities for manufacturing growth and transformation. When manufacturers adopt these technological advancements they will achieve growth and competitive advantage while fostering sustainability.

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.

Models and P4R asset description for digital twin-based advanced planning and scheduling using cyber-physical integration for resilient production operation

Mechanical coffee dryers have been widely adopted to reduce weather dependence, improve yield, and stabilize product quality. However, their operation is still energy-intensive and often suboptimal in terms of controlling the temperature, airflow and moisture content of the grains. In parallel, digital twin (DT) technology has emerged to virtually replicate complex processes and enable model-based monitoring, optimization, and control. This article presents a systematic review based on PRISMA on mechanical coffee dryers and their modeling and control strategies and the current and emerging use of digital twins in drying processes, including agricultural and food products with technological analogies to coffee. The results show a large amount of research on mathematical modeling, energy evaluation, and quality evaluation of mechanical coffee drying. Rapidly growing but still predominantly conceptual literature on digital twins for food processing and drying. Finally, only a small convergence between the two fields, with no fully realized digital twin for mechanical coffee dryers having yet been reported. This review found key gaps in the detection, data infrastructure, and development of hybrid physical-informed AI models. Finally, lines of research are proposed for mechanical coffee dryers enabled with digital twins, aimed at energy efficiency, product traceability and quality assurance.

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.