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.

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.

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.

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 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.