A core technology in realizing Industry 4.0 is the Asset Administration Shell (AAS) as an implementation of the Digital Twin. The AAS enables interoperable data-sharing in a company as well as within a network of companies, creating an AAS dataspace. To create this AAS dataspace, however, data in Systems of Record (SOR) such as ERP, PLM, and quality databases must be available as AASs. SORs typically form data silos that lack interoperability due to custom data models and interfaces that are typically incompatible with the native Industry 4.0 communication. Very little guidance or even tooling is Available on integrating SORs into the AAS dataspace in a reusable and scalable way. This paper shows architectural solutions and concepts to narrow this gap, thus enabling quick integration of existing SORs with the AAS dataspace. Additionally, it provides an open-source implementation bridging this gap. Researchers and practitioners can benefit from the proposed architecture and implementation by applying various use cases of integrating SORs with AAS dataspace.

The integration of diverse data sources into Asset Administration Shell (AAS) is a cornerstone for the advancement of digital twin technologies in industrial ecosystems. To ensure proper data integration, a concept should meet the requirements related to data integration as well as those related to AAS. This study compares three key data integration concepts for AAS-BaSyx Python SDK, BaSyx DataBridge, and the standardized descriptive approach through Asset Interfaces Description (AID) & Asset Interfaces Mapping Configuration (AIMC) submodels. To evaluate the advantages and limitations of each concept, this study employs 12 distinct criteria, assessing how effectively each concept aligns with these criteria. The result provides insights into the unique features of each concept, informing stakeholders about optimal strategies. This study suggests a synergistic approach that combines the advantages of the various methodologies, thereby overcoming individual limitations and enhancing the digital twin ecosystems.

The transition towards Industry 4.0 requires the modernisation of legacy systems. However, this transformation introduces complexity, mainly due to the variety of data formats and interfaces resulting from the heterogeneity of the used components. For seamless interoperability in Industry 4.0 applications, a harmonised data base is of vital importance. The Asset Administration Shell (AAS), as a standardised digital twin of assets, plays a key role in facilitating interoperable, data-centric solutions in the Industry 4.0 landscape. The prospect of automated data integration offers the potential to reduce errors and optimise the process of digitising legacy systems. This raises the question of what extensions and components within the AAS infrastructure are necessary to realise such automation. In response, this paper presents an architectural concept for integrating legacy systems into the AAS framework. Using an articulated robot as a tangible example, the process of interconnecting data points via the OPC UA protocol is illustrated. Additionally, a prototype is presented, capable of enabling vertical data integration via the BaSyx DataBridge, thus showcasing the notable advantages of automating the incorporation of legacy devices into the AAS. The shown solution retains flexibility and is readily applicable to a variety of systems and scenarios as required.

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.

Many Industry 4.0 use cases require the integration of live data, e.g., from sensors and devices. However, the large number of legacy fieldbus protocols and proprietary data formats turns this integration into an effort-consuming task. As the number of digital twins in a factory increases rapidly, data source integration has to scale well. Until now, little guidance is available on how to implement this integration in a scalable and reusable manner for Industry 4.0. To close this gap, we define five architecture blueprints based on our experience in various Industry 4.0 projects. These blueprints detail various integration scenarios differentiated by key attributes like frequency of data consumption and data production. In these architecture blueprints, two core components, the Updater and the Delegator, are identified. By providing and evaluating our open-source implementation of these two components, we show the feasibility of the defined blueprints. Utilizing the provided open-source components and the defined architecture blueprints will benefit practitioners as well as researchers when it comes to data integration with digital twins.

The digitization of value chains is an ongoing challenge in production. The Asset Administration Shell (AAS) aims to address this issue. Besides standardization activities, there is however little architectural guidance on how to bridge the gap between potential AAS use-cases and realization.In this paper, we describe four AAS related use-cases that we derived from 15 application projects which adopted the AAS into industrial contexts. For each of the four use-cases, we devise an architecture blueprint that documents our experiences when applying the AAS. By utilizing these blueprints, practitioners can benefit from our experiences when implementing the AAS and bridge the gap between use-cases and implementation more easily.

The increasing importance of automation and smart capabilities for factories and other industrial systems has led to the concept of Industry 4.0 (I4.0). This concept aims at creating systems that improve the vertical and horizontal integration of production through (i) comprehensive and intelligent automation of industrial processes, (ii) informed and decentralized real-time decision making, and (iii) stringent quality requirements that can be monitored at any time. The I4.0 infrastructure, supported in many cases by robots, sensors, and algorithms, demands highly skilled workers able to continuously monitor the quality of both the items to be produced and the underlying production processes. While the first attempts to develop smart factories and enhance the digital transformation of companies are under way, we need adequate methods to support the identification and specification of quality attributes that are relevant to I4.0 systems. Our main contribution is to provide a refined version of the ISO 25010 quality model specifically tailored to those qualities demanded by I4.0 needs. This model aims to provide actionable support for I4.0 software engineers that are concerned with quality issues. We developed our model based on an exhaustive analysis of similar proposals using the design science method as well as expertise from seasoned engineers in the domain. We further evaluate our model by applying it to two important I4.0 reference architectures further clarifying its application.

Abstract Traditionally, the quality of a software or system architecture has been evaluated in the early stages of the development process using architecture quality evaluation methods. Emergent approaches like Industry 4.0 require continuous monitoring of both run‐time and development‐time quality properties, in contrast to traditional systems where quality is evaluated at specific milestones using techniques such as project reviews. Considering the dynamics and minimum down‐time imposed by the industrial production domain, it must also be ensured that Industry 4.0 system evaluations are continuously performed with high confidence and with as much automation as possible, using simulations, for instance. In this regard, there is a need to develop new methods for continuously monitoring and evaluating the quality properties of software‐based systems for Industry 4.0, which must be supported by automated quality evaluation techniques. In this research we analyze traditional architecture evaluation methods and Industry 4.0 scenarios, and propose an approach based on Digital Twins and simulations to continuously evaluate runtime quality aspects of the architecture and systems of industrial production plants. The evaluation is based on the instantiation of our approach for a concrete demand of an automation plant in the automotive domain.

Art. 106562

Temporal decoupling is a state-of-the-art method to speed up virtual prototypes. In this technique, a process is allowed to run ahead of simulation time for a specific interval called quantum. By using this method, the number of synchronization points, i.e. context switches, in the simulator is reduced and therefore, the simulation speed can be increased significantly. However, using this approach can introduce functional simulation errors due to missed synchronization events. Thus, using temporal decoupling implies a trade-off between speed and accuracy and the size of the quantum must be chosen wisely with respect to the simulated application. In loosely timed simulations most of the functional errors are tolerable for the sake of simulation speed. However, for instance safety critical errors are rare but can lead to fatal results and must be handled carefully. Prior works present mechanisms based on checkpoints (storing/restoring the internal state of the simulation model) in order to rollback in simulation time and correct the occurred errors by forcing synchronization. However, checkpointing approaches are intrusive and require changes to both the source code of all the used simulation models and the kernel of the simulator. In this paper we present a non-intrusive rollback approach for error-free temporal decoupling, which allows the usage of closed source models by using Unix’s fork() system call. Furthermore, we provide a case study based on the IEEE simulation standard SystemC.