Industrial real-time embedded software is usually developed over decades in heterogeneous, multi-disciplinary development teams, inducing major maintenance effort. In addition to a maintenance strategy, increasing variability of products requires a dedicated strategy for the variability management between products and product families. Software product lines (SPLs) provide a proven methodology to address the variability management. Despite the existence of various tools for SPL engineering, none of existing tool kits was matching our requirements especially regarding hierarchical build system based on SCons. We present a variability analysis toolkit which seamlessly integrates with SCons build system, provides a simple integration with CI/CD pipeline, and an option to graphically explore the structure of build system artifacts. Our toolkit automatically calculates key performance indicators for single and multiple product variant graphs. It also provides cluster analysis to find similarities and differences between different product variants. Our initial findings show the acceptance of our Python-based toolkit by the development teams as well as the ability to automatically detect common and variant-specific feature clusters which are useful for further improvement and maintenance of the whole software product line towards an SPL-aware build system.

Over the past decade, modular production systems have garnered significant attention. The immense value of modular automation and engineering approaches has been demonstrated in several pilot applications, particularly through the use of the MTP standard. However, widespread adoption remains limited. Numerous initiatives across various industries are working to address this issue. A key challenge is the MTP interface for process equipment, which, while designed to serve multiple industries, lacks the necessary industry-specific adaptations. This paper presents a solution by integrating industry-specific libraries with MTP, modular automation, and modular engineering concepts. This integration technique has the potential to significantly enhance the applicability and adoption of modular engineering, addressing the current limitations and paving the way for broader implementation across diverse industries. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

The integration of Industry 4.0 technologies into engineering workflows is an essential step toward automating and optimizing plant and process engineering processes. The Asset Administration Shell (AAS) serves as a key enabler for creating interoperable Digital Twins that facilitate engineering data exchange and automation. This paper explores the use of AAS within engineering workflows, particularly in combination with Business Process Model and Notation (BPMN) to define structured and automated processes. We propose a distributed AAS copy-on-write infrastructure that enhances security and scalability while enabling seamless cross organizational collaboration. We also introduce a workflow management prototype automating AAS operations and engineering workflows, improving efficiency and traceability.

Modular production plants are becoming increasingly prevalent, with broad consensus on their various benefits, including reductions in engineering and commissioning costs and time. The Module Type Package serves as a standardized framework for ensuring interoperability between Process Equipment Assemblies and Process Orchestration Layers. However, since the Module Type Package defines only the core process control interfaces for Process Equipment Assemblies, integrating advanced functionalities - such as trending, historical data management, asset or condition monitoring - remains challenging. A dedicated interface is required to facilitate communication between Process Equipment Assemblies and advanced functions, enabling seamless integration. This paper introduces an app-based concept designed to extend Process Equipment Assemblies with enhanced functionalities. Copyright (c) 2025 The Authors. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0/)

Asset engineering process in energy industry domain is a complex multi-disciplinary and multi-organizational process including an iterative exchange of a large number of artifacts. Legacy document-centric exchange process of those artifacts is an error-prone semi-manual process negatively impacting overall project duration and quality. We present results of a collaborative study between ABB and Equinor exploring the potential of the digitalization of this process based on Industry 4.0 technologies, especially the Asset Administration Shell. An exemplary engineering workflow including OPC UA and AutomationML models embedded in Asset Administration Shells was implemented along with novel Asset Administration Shell infrastructure. Despite minor flaws in maturing Industry 4.0 technologies, no major roadblocks were identified.

Control code is designed and implemented for industrial automation applications that manage power plants, petrochemical processes, or steel production. Popular large language models (LLM) can synthesize low-level control code in the Structured Text programming notation according to the standard IEC 61131-3, but are not aware of proprietary control code function block libraries, which are often used in practice. To automate control logic implementation tasks, we proposed a retrieval-augmented control code generation method that can integrate such function blocks into the generated code. With this method control engineers can benefit from the code generation capabilities of LLMs, re-use proprietary and well-tested function blocks, and speed up typical programming tasks significantly. We have evaluated the method using a prototypical implementation based on GPT-4, LangChain, Open-PLC, and the open-source OSCAT function block library. In several spot sample tests, we successfully generated IEC 61131-3 ST code that integrated the desired function blocks, could be compiled, and validated through simulations.

Digital Twin technology plays a crucial role in the implementation of Industry 4.0 by providing means of optimizing operations, improving efficiency, reducing costs, and minimizing risks through its ability to simulate and integrate information silos and perform test scenarios in a virtual environment. As industrial enterprises face the need to align themselves with new information technologies (IT) and respond to variable market demands, the role of Digital Twin in enabling the evolution toward smart manufacturing becomes even more significant. This paper provides an overview of Digital Twin technology, identifies the architectural challenges that it encompasses, and proposes a preliminary cloud-native architecture to address these challenges, laying the groundwork for future research in this field.

In this work we emphasize the role of the engineering process within the industrial automation domain and its underrepresentation in the Industry 4.0 community possibly explained by discrete roots of Industry 4.0. Towards this aim, we revisit the value chain on the leading picture of Industry 4.0 and indicate gaps for "design-to-order" products and projects where requirement artifacts like Piping and Instrumentation Diagrams (P & IDs) or tag lists are exchanged prior to selecting, ordering and building the actual plant. After the understanding of the importance of the engineering process, we explore the opportunities of using Industry 4.0 technology stack to embed engineering information into the digital twin of a process plant. We underline possible synergies with the current developments of the Industry 4.0 community, like the Data Exchange in the Process Industry (DEXPI) and the Module Type Package (MTP) submodel template definitions. Finally, we present possible best practices for embedding existing engineering-related standards into the Industry 4.0 ecosystem and propose tactics and mechanisms for information modeling to accomplish this task in a most efficient and reusable way.

This article shows a practical implementation of how the technologies MTP (Module Type Package) and AAS (Asset Administration Shell) can be combined in a typical engineering workflow. For the practical implementation, the selection of one of the current Software Development Kits (SDKs) is explained, which makes it easier for the developer to generate Asset Administration Shells and to connect to a corresponding server. The corresponding AAS submodel of the MTP is shown and typical use cases between the engineering tool and the AAS server are explained.

This article shows a practical implementation of how the technologies MTP (Module Type Package) and AAS (Asset Administration Shell) can be combined in a typical engineering workflow. For the practical implementation, the selection of one of the current Software Development Kits (SDKs) is explained, which makes it easier for the developer to generate Asset Administration Shells and to connect to a corresponding server. The corresponding AAS submodel of the MTP is shown and typical use cases between the engineering tool and the AAS server are explained.

Large language models (LLMs) providing generative AI have become popular to support software engineers in creating, summarizing, optimizing, and documenting source code. It is still unknown how LLMs can support control engineers using typical control programming languages in programming tasks. Researchers have explored GitHub CoPilot or DeepMind AlphaCode for source code generation but did not yet tackle control logic programming. The contribution of this paper is an exploratory study, for which we created 100 LLM prompts in 10 representative categories to analyze control logic generation for of PLCs and DCS from natural language. We tested the prompts by generating answers with ChatGPT using the GPT-4 LLM. It generated syntactically correct IEC 61131-3 Structured Text code in many cases and demonstrated useful reasoning skills that could boost control engineer productivity. Our prompt collection is the basis for a more formal LLM benchmark to test and compare such models for control logic generation.

Long life cycles of OT equipment used in process industry pose a burden to the adoption of high-paced Industry 4.0 IT applications. NAMUR Open Architecture (NOA) specifies a reference architecture to securely decouple the life cycle of IT and OT components, allowing both to evolve on the required pace. Verification of Request (VoR) and Security Gateway are two key concepts of NOA to allow controlled information traversal both ways between IT and OT domains. In this work we present a cloud-native software architecture of VoR by using a standardized OPC UA PubSub communication over MQTT. The architecture has been successfully validated on a simulated system using Kubernetes clusters and Virtual Machines hosting product-grade DCS and edge components.

In today’s Industrial Internet of Things (IIoT), a broad range of communication protocols are utilized. Built-in security mechanisms enable these protocols to protect communication and defend against network attacks. However, before IIoT devices can utilize these security mechanisms, they need to be securely onboarded in the network. Although several onboarding solutions exist, there is no widely applicable and easy solution for all protocols. Thus, owners of IIoT devices must currently perform multiple processes until they can securely use a device in operation, which requires a high amount of manual effort and onboarding infrastructure.In this work, we present a generic secure onboarding solution for a broad range of network protocols based on OPC UA. OPC UA is particularly suited for this task, as it is one of the most widespread IIoT protocols and one of few protocols whose standard defines a secure onboarding. Our solution leverages the OPC UA onboarding process to equip other IIoT protocols with the initial trust and credentials to establish secure connections. To this end, only minor extensions to the OPC UA implementation on devices are necessary, such that device owners can reuse their OPC UA onboarding infrastructure without any modifications. As a proof of concept for our solution, we demonstrate the secure onboarding of an HTTPS web server. Our implementation fully reuses the reference implementation OPC UA sample server as infrastructure and only needs minor extensions to the IIoT device.

Implementing digital circular economy use cases puts high requirements to lifecycle information of assets, e.g. devices, and to the infrastructure storing and serving this information. We review the requirements from circular economy and compare them with current developments of Industry 4.0. We observe that most gaps regarding circular economy requirements relate to the infrastructure. To close these gaps, we present a vision called Long-Term Storage Digital Twin (LTS Digital Twin) and sketch its use for disassembly data and carbon footprint estimation use cases.

Modularization is a trend to address the requirements for the changeability of industrial production plants. In most cases, a hierarchical orchestration is assumed considering only vertical communication between higher level systems and modules. In practical applications, however, there is a need for cross-communication, as this article demonstrates. Moreover, an information model for the description of cross communication as solution is presented and discussed.

With the increasing demand for customized systems and rapidly evolving technology, software engineering faces many challenges. A particular challenge is the development and maintenance of systems that are highly variable both in space (concurrent variations of the system at one point in time) and time (sequential variations of the system, due to its evolution). Recent research aims to address this challenge by managing variability in space and time simultaneously. However, this research originates from two different areas, software product line engineering and software configuration management, resulting in non-uniform terminologies and a varying understanding of concepts. These problems hamper the communication and understanding of involved concepts, as well as the development of techniques that unify variability in space and time. To tackle these problems, we performed an iterative, expert-driven analysis of existing tools from both research areas to derive a conceptual model that integrates and unifies concepts of both dimensions of variability. In this article, we first explain the construction process and present the resulting conceptual model. We validate the model and discuss its coverage and granularity with respect to established concepts of variability in space and time. Furthermore, we perform a formal concept analysis to discuss the commonalities and differences among the tools we considered. Finally, we show illustrative applications to explain how the conceptual model can be used in practice to derive conforming tools. The conceptual model unifies concepts and relations used in software product line engineering and software configuration management, provides a unified terminology and common ground for researchers and developers for comparing their works, clarifies communication, and prevents redundant developments.

These artifacts relate to the VaMoS'22 research paper "Unified Operations for Variability in Space and Time".

Die Implementierung von digitalen Anwendungsfällen der Kreislaufwirtschaft stellt hohe Anforderungen an die Lebenszyklusinformationender Assets, z. B. Geräte, und an die Infrastruktur, die diese Information speichert und bereitstellt. In diesem Beitragwerden die Anforderungen der Kreislaufwirtschaft diskutiert und mit den aktuellen Entwicklungen im Bereich Industrie 4.0abgeglichen. Wir stellen fest, dass die meisten aufgezeigten Lücken die Infrastruktur betreffen. Um diese Lücken zu schließen, wird eine Vision des Long Term Storage Digitalen Zwillings (oder LTS Digitalen Zwillings) vorgestellt und dessen Nutzung anhand von Use-Cases der Demontagedaten und der Ermittlung des CO2-Fußabdrucks skizziert.

Simulation is a key technology for Industry 4.0 which helps achieving several of Industry 4.0 use cases like flexible production or product design. However, simulating complex industrial systems with several modules and sub-systems is a complicated task which includes co-operation and orchestration of separated simulation models. Therefore, this work proposes a solution that enables simulation interoperability and orchestration using Asset Administration Shell (AAS)-based semantic descriptions. The proposed approach is also verified via a real scenario that enables dimensioning of drives in a drive-train while controlling the holistic simulation.

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.