Data integration remains a major challenge in the industry due to the heterogeneity of systems, data sources, and communication protocols. As the Asset Administration Shell becomes a widely adopted standard for digital representations, there is an increasing need for dynamic and user-friendly data integration solutions tailored to it. Existing methods often require extensive programming knowledge, which limits accessibility and slows down deployment. This work presents a low-code approach to industrial data integration based on the Asset Administration Shell, leveraging the standardized Asset Interfaces Description and Asset Interfaces Mapping Configuration submodels, and following an Extract, Transform, Load process. We introduce a Python-based extractor that reads metadata from Asset Interfaces Description and Asset Interfaces Mapping Configuration submodels to automatically generate Node-RED flows with minimal user input. This approach enables real-time data exchange by configuring communication nodes and mappings without manual programming. Overall, our work lowers the barriers to industrial data integration, empowers non-technical users to configure and deploy integration workflows more efficiently, and fosters interoperability through standardized data models.

The Asset Administration Shell is emerging as the standardized digital twin representation for Industrie 4.0 assets, enabling interoperability across organizational and technical boundaries. However, existing access control mechanisms fail to satisfy the Asset Administration Shell’s needs for fine-grained, context-aware, cross-domain security. This paper proposes an access control concept tailored to the Asset Administration Shell, combining Role Based Access Control and Attribute Based Access Control to meet a set of ten key requirements derived from standards, literature, and expert interviews. Our approach follows a technology-neutral architecture, with a concrete prototype implementation using Keycloak as identity provider, Envoy for request interception, and Open Policy Agent for centralized policy based decision-making; thereby separating security concerns from application logic. An evaluation against the defined requirements shows that this prototype meets nine of the ten requirements. Overall, this concept lays the groundwork for future development of secure, scalable, and flexible access control solutions in industrial Asset Administration Shell deployments.

This paper presents a novel approach for the semantic comparison of Asset Administration Shells (AAS), i.e., digital twins within the context of Industry 4.0. As digital twins gain importance in optimizing industrial processes, ensuring interoperability and accurate versioning of AAS becomes critical. We propose a method that focuses on object model-level comparisons rather than mere syntactic differences, enabling a more meaningful assessment of semantic equivalence. Our findings highlight the challenges associated with traditional comparison methods and suggest a comprehensive framework for enhancing AAS versioning and integrity verification.

Despite the advancements brought by digitalization across industries, only a few state-of-the-art data-driven methods successfully transition to production and remain viable.The sheer volume of physical assets in production lines, combined with constantly evolving requirements, makes model deployment and maintenance highly complex.This paper presents a production-ready architecture developed for data-driven digital assets at ABB Schaffhausen AG.The solution integrates MLOps best practices orchestrated via MLRun with the industry-standard metadata modeling system, Asset Administration Shell (AAS).We demonstrate how controlled artifact generation from MLRun facilitates experiment tracking and knowledge sharing while AAS ensures standardization and long-term maintenance.By combining MLOps and AAS, we effectively manage the ever-growing artifacts of data-driven solutions.Additionally, we explore how controlled artifact generation enables role-based MLOps by restricting access to relevant information based on industrial roles.This architecture supports a smooth transition to Industry 5.0.

Reliability engineering plays a crucial role in modern industrial systems, aiming to minimize costly downtime and prevent safety hazards. The feasibility of automating this process largely depends on the available data. While sensor-level data analysis can reveal crucial insights into system health and operational states, often only event-driven log data is available due to technical or cost constraints. Although extensive research on log-based failure diagnosis has been conducted, particularly in the information technology (IT) sector, the application of these methods remains challenging in real-world industrial systems. Hence, we propose a modular and interpretable framework for log-based anomaly detection in industrial systems to address the interpretability and reliability shortcomings of previous approaches. The results obtained from a real-world production system validate the framework’s ability to support timely root cause analysis and facilitate predictive maintenance.

The flexible allocation and configuration of modular production resources are key enablers for sustainable, reconfigurable, and scalable manufacturing systems. Ensuring that process requirements can be satisfied by available modular resources, while adhering to all operational and connectivity constraints, presents a significant challenge for automation engineering. This contribution introduces a semantics-driven approach for the automated feasibility checking of modular plant configurations. Formal process requirements, expressed as general recipes compliant with the ISA-88 standard and enriched with semantic capability identifiers, are transformed into logical constraints. Resource descriptions, modeled via the Asset Administration Shell (AAS), provide machine-readable capability and property information. Both representations are mapped into a formal Satisfiability Modulo Theories (SMT) model using the open-source SMT4ModPlant framework, relevant for cyber-physical and IoT-enabled manufacturing systems. The approach enables the automated feasibility checking of process-to-resource assignments, property compatibility, and material flow consistency by leveraging the Z3 SMT solver. Validation with a modular process scenario demonstrates that the proposed method identifies feasible configurations and provides transparent explanations for assignment decisions.

Abstract Software verifiers have different strengths and weaknesses, depending on the characteristics of the verification task. It is well-known that combinations of verifiers via portfolio- and selection-based approaches can help to combine their strengths. In this paper, we investigate (a) how to easily compose such combinations from existing, ‘off-the-shelf’ verifiers without changing them and (b) how much performance improvement each combination can yield, regarding the effectiveness (number of solved verification tasks) and efficiency (consumed resources). First, we contribute a method to systematically and conveniently construct verifier combinations from existing tools using CoVeriTeam. We consider sequential portfolios, parallel portfolios, and algorithm selections. Second, we perform a large experiment to show that combinations can improve the verification results without additional computational resources. Our benchmark set is the category ReachSafety as used in the 11th Competition on Software Verification (SV-COMP 2022). This category contains 5 400 verification tasks, with diverse characteristics. The key novelty of this work in comparison to the conference version of the article is to introduce a validation step into the verifier combinations. By validating the output of the verifier, we can mitigate the adverse effect of unsound tools on the performance of portfolios, especially parallel portfolios, as observed in our previous experiments. We confirm that combinations employing a validation process are significantly more robust against the inclusion of unsound verifiers. Finally, all combinations are constructed from off-the-shelf verifiers, that is, we use the verification tools as published. The results of our work suggest that users of combinations of verification tools can achieve a significant improvement at a negligible cost, and more robustness by using combinations with validators.

In this work, we present Fences, a tool to generate sample data for arbitrary JSON Schemas. Fences generates these samples in three basic steps: First, the schema is simplified and normalized using boolean algebra. Second, the schema is mapped to a flow graph. And third, the flow graph is analyzed to obtain the final sets of JSON data. Besides valid samples, Fences also generates invalid samples it knows to be rejected by the schema. By combining these sets, all degrees of freedom offered by the schema are covered systematically. We assess the feasibility and correctness of our approach using synthetic schemas from the official JSON Schema test suite and a real-world use case from the Industry 4.0 domain. Additionally, we introduce schema coverage as metric to assess the completeness of our approach.

In the research area of capabilities and skills, the standardization of terms and models is progressing in working groups of Plattform Industrie 4.0 and Industrial Digital Twin Association. At the same time, there is an increasing amount of publications that use capability models for purposes such as automated selection of capabilities or planning of sequences. However, there is currently no concept and formal description of effects generated by resources when acting on products. Having such a formal effect definition is needed to express both required effects as well as effects achieved by capabilities in order to compare requirements with possible solutions to a desired production step in an automated manner. This article introduces a formalism that can be used to define effects in the context of capabilities. An effect is seen as the change between an initial and a target state, which can both be specified using properties. The formalism is used to express effects in three different manufacturing application examples. Furthermore, an implementation of the formalism using the Web Ontology Language is provided. This ontology is used to model effects in a machine-interpretable way and to automatically compare effects for compatibility.

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.

As Industrie 4.0 (I4.0) technologies continue to advance, ensuring the reliability and robustness of emerging standards like Asset Administration Shells (AAS) becomes paramount. This paper introduces a comprehensive testing approach for reactive (a.k.a. Type 2) AAS implementations, addressing the challenges posed by their intricate and dynamic nature. Our methodology adopts a design-by-contract approach to formalize API behavior. We design the client as a test oracle, where code contracts extensively validate the AAS operations, their interactions and effects. The related test cases cover various operation chains, including superpaths, service specifications, and serialization modifiers. The objective of this approach is to enhance the reliability and effectiveness of testing in the evolving landscape of AAS Type 2 implementations.

Current discourse of communicative features of Asset Administration Shells (AAS) leaves an important aspect unaddressed: Should submodels be designed to be usable independently from their overarching AAS? Permitting such independent usage allows for more flexible physical deployment of individual AAS submodels in computer networks or on information media. In this paper, a presentation and classification of possible deployment variants is given, based on this principle. In addition, different requirements on the AAS infrastructure are discussed and how the deployment variants can contribute to their fulfillment. Finally, possible prioritization of infrastructure requirements and resulting submodel deployment are shown in three concrete usage scenarios.

In a significant move towards enhancing flexibility in industrial automation, the development of adaptable and scalable control components (CCs) as control-related skills in process and manufacturing industry aims at supporting effective management of complex production systems. This research seeks to develop CC architecture design methods. As a basis, a unified library of elementary functions is designed to enable creation of CCs. With a focus on enabling dynamic CC composition and skill generation, we aim to provide a systematic and scalable approach to flexibility in control applications. The primary objective is to establish a proof of concept for the proposed function library, demonstrating its versatility and effectiveness via validating various procedures. Through the combination of a conceptual framework and prac-tical implementations, this paper validates the potential impact of a unified, composable CCs in enhancing adaptability and re-sponsiveness within control applications in industrial production. Furthermore, the machine-readable functionality description of CCs enhances their reusability and adaptability and thus increase the flexibility and efficiency of production processes. This lays the basis for semantic interoperability which is required to integrate the CC framework into capability-based control application configuration, particularly within the Industry 4.0 framework.

This paper introduces a high-level language compiler with IEC 61131–3 compliance capable of converting control function code written in Python into structured text. The Python-to-Structured Text Compiler aims to bridge the gap between modern language programming and PLC programming in industrial automation. The proposed Py2ST Compiler workflow includes parsing, transformation, code generation, and code import stages. The implementation of these phases is presented in this paper. And the compilation ability of the Py2ST Compiler is validated by a case study. Utilizing the Py2ST Compiler facilitates the development of PLC control functions within the Python environment. This capability enables the management of both IT and OT functions within a unified environment, enhancing the overall project's maintainability and reducing the development cycle.

This paper presents the design, implementation, and validation of a Control Function Library to support standardized design and deployment of control functions for automated industrial process operation. The Control Function Library establishes a standardized set of control functions for chemical production. Five categories (Time-based, Continuous, Reversible, Stopping, and Ramping control functions) are developed and encapsulated in the first version of the library. The Control Function Library enables scalability by providing a flexible framework for adding or modifying control functions to accommodate specific production requirements. This scalability allows control systems to adapt to changing requirements or environments without significant redevelopment efforts, especially for capability-based operation configuration and execution. Furthermore, the reuse of Control Function Libraries across various projects or applications minimizes development time and resource costs by avoiding the need to re-engineer processes from scratch for each new modular plant.

Digital twins, the virtual representations of physical assets, processes or systems, are becoming increasingly important in cyber-physical systems. They are a foundational block of Industry 4.0, the movement to digitalize industrial processes.The Asset Administration Shell (AAS) has been established as the preferable model to capture interoperable digital twins in the context of Industry 4.0. As asset administration shells become more prevalent, so does the need for specialized software development kits (SDKs) to manage them.The intertwined Industry 4.0 value chains are highly dependent on interoperability. The SDKs for AAS are often the direct interface between the components, and the malfunctioning of the SDKs leads to the breakage of the value chain. It is therefore important that we have the tools to determine how individual SDKs behave, and detect when they malfunction. In this work, we implement an approach to assessing the maturity of the SDKs for managing asset administration shells, and perform a thorough survey and evaluation of the existing SDKs.

Asset Administration Shells are an essential component of Industry 4.0, and submodels are crucial to their functioning. However, the current method of generating submodels involves writing custom code, which can lead to code duplication and inconsistencies across submodels. To address these challenges, we propose an automatic generation of submodel-specific classes with predefined meta-information based on submodel templates. Our proposed solution aims to reduce code duplication, provide support in integrated development environments, ensure consistency across submodels, and facilitate the immediate publishing of submodel software development kits. This paper discusses the current approaches for instantiating submodels and their problems, the proposed approach, its requirements, advantages, and an outlook.

The Asset Administration Shell offers the capabilities for unified and interoperable data exchange across the entire life-cycle of an asset. Therefore, using Asset Administration Shells as a data layer is a practical option for facilitating the exchange of information in Simulation-based Engineering. The feasibility of this approach is demonstrated through an implementation, showing that incorporating the Asset Administration Shell as a data layer for standardized data exchange can yield significant benefit for the Simulation-based Engineering process.

The contribution addresses the challenge of transforming a general recipe, which lacks knowledge about specific production resources, into a plant-specific master recipe in the process industry. The authors propose an approach using the concept of capabilities in manufacturing, enabled by an ontology and the Asset Administration Shell. The approach aligns required and offered capabilities at different description levels to allocate equipment to process elements and generate a master recipe in BatchML. The validation of the approach is accompanied by a procedure logic for a modular plant and discusses open challenges. In conclusion, the provided approach offers a methodical and less experience-dependent derivation of a master recipe for a manufacturing process.

The Asset Administration Shell is the key concept for the exchange of asset information in Industrie 4.0 across the entire life cycle of an asset. Therefore, the Asset Administration Shell acts as the central interface to access asset information, which is stored in different source systems, e.g. databases or control units. To achieve this vision, this paper presents the requirements for the standardized integration of data from these source systems into Asset Administration Shell applications and a generic concept for easy and vendor-independent applicability. The concept is demonstrated with two example implementations and evaluated against the requirements.

Digital twins are a fundamental building block for the Industrie 4.0. With the Asset Administration Shell, a standard has been published that describes a meta model of such digital twins and the interfaces to access them. One of these interfaces is file-based access. Since the advent of the standard, various implementations have been developed. To ensure interoperability between different implementations, compliance with the specification must be verified. To test the correctness of a compliance check tool, a set of digital twins with known properties is used as test input. Since manually specifying such test models is error-prone, this work contributes an automated generation approach. It leverages flow graphs to represent the space of possible models. The subsequent path selection aims to generate expressive, yet efficient test input.

The digital twin, or more precisely the Asset Administration Shell, is the key element for interoperability in Industrie 4.0. Together with the asset, it forms the I4.0 component. For the development of I4.0 components, the availability of suitable software development tools is becoming increasingly relevant. However, it is not yet specified how these tools are to be systematically tested for correctness, compliance and conformity. These topics are the focus of this work. We show different established testing methods, evaluate their suitability and describe an approach for a capable test environment.

Modular automation is an upcoming paradigm to improve flexibility and maintainability of industrial production systems. The lack of interoperability between equipment adhering to different modular automation specifications limits module sourcing and hampers coexistence of discrete and process modules in joint hybrid production applications. In this work, we present a reference software architecture for modular automation which is able to cover existing specifications and demonstrate how it can be used to assess alternatives of cross-standard orchestration. Based on this foundation, next steps regarding cross-module and cross-orchestrator communication are outlined.

Interconnectedness is an important feature of components in future industrial domains. Internet of Things, Industry 4.0, Cyber Physical Systems, autonomous driving, etc. are some new paradigms that require interconnectedness as an important basis. The interconnectedness plays a crucial role to fulfill the future requirements but at the same time can cause unauthorized access to the systems and increases the probability of system attacks. Future systems consist of large number of components that communicate with each other. These components have different requirements (i.e. availability, real-time, security). Especially, the communication and information exchange with high critical components must be controlled. This paper addresses a secure and unidirectional communication between high critical components and components with lower level of criticality on the same hardware.

Promoting digitalization in industrial automation drives usage of new technologies, communication and information models. Thereby providing a platform for industrial automation to interact cooperatively with information technologies plays a crucial role to increase its digitalization and intelligence. An important factor in this interaction is security. The current automation systems do not cooperate in a high interconnected environment but offer a high security level. In contrast to the current automation systems, the future automation systems in context of Industry 4.0 offer high interconnectedness but require a security solution. The interaction deals with retrieving and processing data from the process control system and sending feedback to it. For retrieving data from automation systems different concepts are discussed such as the NAMUR Information diode. The goal of this paper is to develop a realization of a secure gateway for the interaction of information technology and automation systems. Furthermore, an approach to integrate feedback of information technology into process control is discussed.

Industrial automation architectures have been evolving due to the new requirements in context of Industry 4.0 or other similar paradigms. Interconnectedness and modularity are two factors that play a crucial role in fulfilling these new requirements. The features must be applied to different automation levels and domains. This paper focuses on a service based interaction of MES and process control in a modular an interconnected environment to promote process optimization. Another important factor that must be considered is security. Linking the industrial automation with IT technologies is a basis for optimization and digitalization but it exposes the system to security threats. Retrieving process information for further data processing and online system optimization including the controlled process must be accomplished via an approach that ensures the security requirements of the system. This paper discusses such a secure approach for the interaction between process control, MES and a digital twin application.