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Within the context of the Sustainable Product Initiative of the EU, product passports will likely be required in more and more sectors. However, their introduction is associated with challenges considering data collection and technical implementation, as the degree of standardization and availability of technical implementations for product passports is still low. To address these challenges, this work presents a digital product passport using asset administration shells to enable standardized connectivity via REST interfaces and, thus, standardized information exchange. The data model created contains submodels that specify information concerning the entire lifecycle of the product. Furthermore, these data enable the determination of the carbon footprint of the products as well as remanufacturing. The product passport will be implemented as a Gaia-X service to enable a secure and decentralized collection, exchange, and storage of product related data. The developed digital product passport and the Gaia-X ecosystem will be set up as demonstrators in the Learning Factory Global Production. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper extends the traditional factory digital twins by incorporating human characterisation in Asset Administration Shell (AAS). The extension lays the basis for human-centred control and management, as demonstrated by employing a prototype of the extended AAS in two proposed use cases. Referred to Industry 5.0, an accurate digital representation of humans as a basis of the data-based decision support to improve operators' well-being and resilience. The AAS is extended to include dedicated digital models accommodating a set of properties to describe the human operators and its interactions with the surrounding shop-floor resources. Two reference use cases have been designed in the context of a complete lab-scale manufacturing system: equipment and devices have been modelled according to the AAS standard, exposing information via MQTT, and have been integrated with the proposed AAS definition of human operators. Operators have been equipped with wearable sensors and a dashboard providing them with feedback from the manufacturing environment and notifications about changes. As part of the extension process, some ethical and regulation concerns are discussed, highlighting that the extended AAS is mature enough to support the inclusion of human operators, but regulations struggle to keep up with technological advances. [ABSTRACT FROM AUTHOR]

Translation abstract Additive flow production of lightweight precision concrete elementsIn modern flow production, the focus is on the continuously digitally controlled tools in the context of the digital twin. Spray extrusion, especially when combined with a thin layer of shotcrete and ribbed support structures, e. g. for façade elements, results in highly efficient components with high load‐bearing capacity and high‐quality surfaces. The developed combined spray extrusion nozzle offers the possibility of both spraying and extrusion, adaption of mortar and concrete. In addition, the automated integration of anchors is the focus of flow production, for which anchors and tooling systems have been developed. Based on the determination of characteristic values, the practicability and the increase in productivity will be proven. This data will form the basis of a digital twin, in preparation for Industry 4.0. This increase in productivity in additive manufacturing leads to positive economic effects. Applications of high‐strength lightweight structures in the construction industry are becoming more attractive and enable the serial implementation of free‐form modular constructions. [ABSTRACT FROM AUTHOR]

The emerging digitalization in today's industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system's decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection. [ABSTRACT FROM AUTHOR]

ABSTRACTProduction Planning and Control involves combinatorial optimization problems subject to domain-related constraints. Hence, decision-support systems are required to support operators in finding suitable production scheduling models that match the conditions on the real shopfloor. Digital Shadows were introduced to solve this task by automatically gathering suitable models and the required input data to provide decision-support. Therefore, a standard definition of production scheduling models and their grouping within a model catalog is required in the virtual world. In this contribution, we introduce an Asset Administration Shell submodel that defines production scheduling models based on Graham’s three-field notation, which is independent of the underlying production process. We consider metadata, which ensures those models are FAIR, i.e. findable, accessible, interoperable, and reusable. In addition, we build a model catalog for production scheduling models in the injection molding domain as the foundation for implementing a decision-support system based on the Digital Shadows.

To facilitate the shift from a Linear to a Circular Economy, it is essential to alter the manner in which we manufacture, use, and dispose of products. Interoperable information exchange among various stakeholders throughout the product lifecycle is crucial if we wish to encourage reuse, repair, remanufacturing, refurbishing, or recycling. The Digital Product Passport (DPP) is a concept for capturing product information. This paper presents an end-to-end process for sharing product-related information along the product lifecycle explicitly designed to cater to the needs of all stakeholders, including end-of-life actors. This process is based on a DPP concept and implementation called the Digital Lifecycle Passport and a multi-stakeholder platform. Technically, it relies on the Asset Administration Shell (AAS), which enables information to be shared in a structured way. We demonstrate how this process can improve waste sorting and how it can be used for life cycle assessment (LCA). LCA results are captured in an interoperable way based on DIN SPEC 91474, which will facilitate automatic documentation for regulatory purposes in the future. Our approach is a forerunner in the sense that it uses the DPP and the AAS for end-of-life management. [ABSTRACT FROM AUTHOR]

The concept of digital twins is one of the fundamental pillars of Industry 4.0. Digital twin allows the realization of a virtual model of a real system, enhancing the relevant performance (e.g., in terms of production rate, risk prevention, energy saving, and maintenance operation). Current literature presents many contributions pointing out the advantages that may be achieved by the definition of a digital twin of a water supply system. The Reference Architecture Model for Industry 4.0 introduces the concept of the Asset Administration Shell for the digital representation of components within the Industry 4.0 ecosystem. Several proposals are currently available in the literature considering the Asset Administration Shell for the realization of a digital twin of real systems. To the best of the authors' knowledge, at the moment, the adoption of Asset Administration Shell for the digital representation of a water supply system is not present in the current literature. For this reason, the aim of this paper is to present a methodological approach for developing a digital twin of a water supply system using the Asset Administration Shell metamodel. The paper will describe the approach proposed by the author and the relevant model based on Asset Administration Shell, pointing out that its implementation is freely available on the GitHub platform. [ABSTRACT FROM AUTHOR]

This study addresses the challenge of the optimization of milling in industrial production, focusing on developing and applying a novel framework for optimising manufacturing processes. Recognising a gap in current methods, the research primarily targets the underutilisation of advanced data analysis and machine learning techniques in industrial settings. The proposed framework integrates these technologies to refine machining parameters more effectively than conventional approaches. The research method involved the development of the framework for the realisation and analysis of measurement data from milling machines, focusing on six machine parts and employing a machine learning system for optimization and evaluation. The developed and realised framework in the form of a software demonstrator showed its applicability in different experiments. This research enables easy deployment of data-driven techniques for sustainable industrial practices, highlighting the potential of this framework for transforming manufacturing processes. [ABSTRACT FROM AUTHOR]

Copyright of Automatisierungstechnik is the property of De Gruyter and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This research introduces a novel approach for achieving semantic interoperability in digital twins and assisting the creation of Asset Administration Shell (AAS) as digital twin model within the context of Industry 4.0. The foundational idea of our research is that the communication based on semantics and the generation of meaningful textual data are directly linked, and we posit that these processes are equivalent if the exchanged information can be serialized in text form. Based on this, we construct a “semantic node” data structure in our research to capture the semantic essence of textual data. Then, a system powered by large language models is designed and implemented to process the “semantic node” and generate standardized digital twin models (AAS instance models in the context of Industry 4.0) from raw textual data collected from datasheets describing technical assets. Our evaluation demonstrates an effective generation rate of 62-79%, indicating a substantial proportion of the information from the source text can be translated error-free to the target digital twin instance model with the generative capability of large language models. This result has a direct application in the context of Industry 4.0, and the designed system is implemented as a data model generation tool for reducing the manual effort in creating AAS model by automatically translating unstructured textual data into a standardized AAS model. The generated AAS model can be integrated into AAS-compliant digital twin software for seamless information exchange and communication. In our evaluation, a comparative analysis of different LLMs and an in-depth ablation study of Retrieval-Augmented Generation (RAG) mechanisms provide insights into the effectiveness of LLM systems for interpreting technical concepts and translating data. Our findings emphasize LLMs’ capability to automate AAS instance creation and contribute to the broader field of semantic interoperability for digital twins in industrial applications. The prototype implementation and evaluation results are presented on our GitHub Repository: https://github.com/YuchenXia/AASbyLLM.

In a re-entrant job shop (RJS), an entity can visit the same resource type multiple times; this is called re-entrancy, which occurs frequently in actual industries. Re-entrancy causes an NP-hard problem and is dominated by heuristics-based production control. The stochastic arrivals due to re-entrancy require the design of an appropriate dispatching rule. Reinforcement learning (RL) is an efficient technique for establishing robust dispatching rules; however, only a few cases that coordinate RL-based production control with a digital twin (DT) have been reported. This study proposes a novel production control model that applies a DT and horizontal coordination with RL-based production control. The requirements for dispatching in the RJS and coordination between RL and the DT were defined. A suitable architectural framework, service composition, and systematic logic library schema were developed to exploit the advanced characteristics of the DT and improve the existing production control methods. This study is an early case of coordinating RL and DT, and the findings revealed that RL policy networks should be imported in the creation procedures rather than being synchronised to the DT. The results should be a valuable reference for research on other types of RL-based production control with regard to horizontal coordination. [ABSTRACT FROM AUTHOR]

Ever-changing requirements, conditions, and environments call for a change in manufacturing. Hence, research has increased on new forms of manufacturing, such as distributed manufacturing systems. However, distributed manufacturing systems require an IT architecture that covers real-time monitoring, management, and analysis of geographically distributed production sites. Digital twins allow for such a systematic data control and shopfloor management. They enable complex distributed manufacturing systems to be more autonomous and to be connected, adding value to the production system. Even though there have been research activities and publications on this topic, a systematic review of the current state of specific technologies used for distributed manufacturing is yet missing. This paper reviews and categorizes publications to identify relevant technologies for building digital twins supporting the distributed manufacturing paradigm and objectives of digital twins for distributed manufacturing. [ABSTRACT FROM AUTHOR]

The manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies' business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model's application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0. [ABSTRACT FROM AUTHOR]

In modern flow production, the focus is on the continuous digitally controlled tools within the context of the digital twin. Spray extrusion, especially in combination with a thin layer of shotcrete and ribbed supporting structures, for example for facade elements, leads to very efficient components with high load‐bearing capacity and high quality surfaces. The developed combined spray extrusion nozzle offers the possibility of both: spraying and extruding, adapting mortar and concrete. In addition, automated integration of anchors is the focus of flow production, for which anchors and tooling systems have been developed. Based on the determination of characteristic values, the practicability and the increase in productivity could and will be proven. This data forms the basis of a digital twin in preparation for Industry 4.0. This increase in productivity in additive manufacturing leads to positive economic effects, applications of high strength lightweight structures in construction are becoming more attractive and enable the series implementation of free form modular constructions. [ABSTRACT FROM AUTHOR]

Personalised production allows the supply chain (SC) to exist in various dynamic fluctuations within a make-to-order (MTO) environment. An SC for personalised production has redundant inventory and operation capacity; therefore, it requires a system that can achieve recoverability for operation resilience. Thus, a standalone cyber physical system (CPS) has limitation for SC control with MTO. To solve this problem, the CPS must be coordinated, and a systematic approach is required. This study proposes a cyber physical logistics system (CPLS) that is coordinated with the agent cyber physical production systems in a multi-level CPS structure. This multi-level architectural framework is designed to provide technical functionalities for resilient SC control. The service composition procedures of technical functionalities on the distributed digital twin (DT) simulation are divided into type and instance stages. The operation procedures of the DT application and technical functionality of the DT engine are suggested. The proposed CPLS has appropriate service composition and operation for the bullwhip and ripple effects, which are the two main SC-related problems. This study illustrates an early case of CPLS that can minimise differences among assets using distributed DT simulation; furthermore, the study establishes an SC and production plan based on the DT simulation results. [ABSTRACT FROM AUTHOR]

The emerging digitalization in today’s industrial environments allows manufacturers to store online knowledge about production and use it to make better informed management decisions. This paper proposes a multi-agent framework enhanced with digital twin (DT) for production scheduling and optimization. Decentralized scheduling agents interact to efficiently manage the work allocation in different segments of production. A DT is used to evaluate the performance of different scheduling decisions and to avoid potential risks and bottlenecks. Production managers can supervise the system’s decision-making processes and manually regulate them online. The multi-agent system (MAS) uses asset administration shells (AASs) for data modelling and communication, enabling interoperability and scalability. The framework was deployed and tested in an industrial pilot coming from the bicycle production industry, optimizing and controlling the short-term production schedule of the different departments. The evaluation resulted in a higher production rate, thus achieving higher production volume in a shorter time span. Managers were also able to coordinate schedules from different departments in a dynamic way and achieve early bottleneck detection.

ABSTRACTThe manufacturing industry faces a new revolution, grounded on the intense digitalization of assets and industrial processes and the increasing computation capabilities imposed by the new data-driven digital architectures. This reality has been promoting the Digital Twin concept and its importance in the industrial companies’ business models. However, with these new opportunities, also new threads may rise, mainly related to industrial data protection and sovereignty. Therefore, this research paper will demonstrate the International Data Spaces reference model’s application to overcome these limitations. Following a pilot study with a Portuguese machine producer/maintainer enterprise, this paper will demonstrate the development of a cutting and bending machine Digital Twin, leveraged on an International Data Spaces infrastructure for interoperability, for the plastic and metal industry and its importance to introduce this machine manufacturing company in a new business-to-business marketplace from the EU project Market 4.0.

Although standards and specifications for digital twins aim to create interoperability in Industry 4.0, each standard has its own goals, focuses and representations for digital twins. This paper examines an approach to increasing interoperability between established digital twin specifications by transformation. Accordingly, several specifications are presented and requirements for transformation are examined. Following the feasibility analysis, a mapping between the Digital Twin Definition Language (DTDL) and Asset Administration Shell (AAS) was created. To examine the feasibility of this approach, the transformation was implemented and tested for a physical asset. This paper demonstrates that a generic mapping between DTDL and AAS can be applied for transformation in use cases where DTDL models are provided while AAS is required. [ABSTRACT FROM AUTHOR]