In the context of cross-disciplinary system engineering, seamless data interoperability between modelling tools remains a critical challenge. This paper presents an approach for enabling round-trip engineering between Enterprise Architect (EA) and AutomationML (AML), two widely adopted modelling platforms. The study focuses on modelling a conveyor-based production system using Systems modelling Language (SysML) within EA and subsequently mapping it to AML using an intermediate transformation layer. The transformation is realized with a data transformation pipeline, facilitating export of EA models to AML and import of AML data into EA. The proposed solution demonstrates the potential for streamlined interoperability, reduced manual effort, and enhanced traceability across engineering domains. The findings are validated through a lab-scale case study, reinforcing the possibility of automated bidirectional model exchange for engineering production systems.

Model-Based Systems Engineering (MBSE) relies on precise, unambiguous requirements; however, stakeholders often express needs in free-form natural language, leading to ambiguity and rework. This paper introduces an AI assistant powered by a Large Language Model (LLM) to support the formalization of such requirements. Guided by writing rules from industry standards such as INCOSE and IREB, the assistant identifies ambiguities, classifies requirement types, and rewrites them into consistent, standards-compliant statements. The structured requirements are serialized into SysML v2 using the Kernel Modeling Language (KerML), enabling seamless integration into MBSE workflows. A lightweight Streamlit interface allows users to upload, review, and export refined requirements. The approach is demonstrated through a practical case study, where requirements from educators and engineers are processed using the AI assistant. This work illustrates how LLM can enhance the front end of MBSE by bridging the gap between informal stakeholder input and formalized system specifications.

Efficient engineering data logistics remains a critical challenge in Production Systems Engineering, where diverse data formats and heterogeneous toolchains hinder seamless data exchange across disciplines. Manual and error-prone data transfers increase engineering overhead and pose risks to data consistency. This paper addresses these challenges by proposing the Integrated Engineering Data Transformation (IEDT) framework, which consolidates heterogeneous discipline-specific engineering artifacts into a unified AutomationML (Automation Markup Language)-based model. The framework supports both automated data integration and round-trip export back into native formats, thereby enhancing interoperability and traceability. A supporting software tool, PPR-AML, has been developed to operationalize the framework, facilitating automated data transformation and integration. The framework and tool are validated using a representative use case involving a model production plant, demonstrating their feasibility and effectiveness for streamlining engineering data logistics across disciplines.

In the contemporary manufacturing sector, efficiency and precision are paramount but often hindered by manual and error-prone transfers of data across disparate engineering disciplines. This paper addresses the critical challenges posed by the diversity of data formats and the high reliance on manual processes in engineering data logistics. We propose a novel strategy leveraging Automation Markup Language (AutomationML) to foster a more efficient and error reduced data exchange framework. AutomationML is utilized for its robust representation of plant engineering information, promoting seamless interaction among various engineering tools and systems. By transitioning from existing pipeline-based data logistics methodology to a centralized data storage approach, our method markedly diminishes the likelihood of information loss and reduces integration complexity. This research not only maps out the prevailing issues with current data logistics practices but also introduces a scalable, adaptable tool designed to optimize data integration and transform the engineering workflow. By addressing these potential advancements, the approach sets a path towards a more efficient and effective engineering data logistics paradigm.

In production systems engineering, effective conceptualization is paramount for devising efficient and optimized systems as the majority of project costs are being fixed in this design phase. The Product-Process-Resource (PPR) modeling approach has the potential to enhance this conceptualization by integrating with established Model-Based-Systems-Engineering (MBSE) frameworks. To ensure that interaction, the role of PPR modeling in facilitating early-stage design decisions and fostering a systems-thinking approach throughout the design process has to be investigated. This paper demonstrates the efficacy of PPR modeling in improving the conceptualization phase of production system design, followed by its integration into MBSE frameworks. An industrial case study illustrates how such an approach enables engineers to efficiently visualize and analyze the complex interactions between product components, manufacturing processes, and resource allocations, thereby creating more informed design decisions. Furthermore, the paper discusses challenges and opportunities associated with integrating PPR modeling within MBSE frameworks and proposes future research directions.

AutomationML (AML), a standard for the interoperable exchange of engineering data in industrial automation, holds promise for facilitating seamless integration and data exchange across diverse industrial systems. By serializing AML to JSON, the format aims to enable a wide array of functionalities associated with JSON. Yet, such adoptions need to be carefully considered in order to have the support of the modeling community. This paper presents the efforts of the AML-JSON working group to define this serialization. It discusses the methodology employed, the technical aspects of the serialization process, and the implications for industrial applications. Furthermore, it highlights the advantages and challenges of adopting a JSON serialization within the context of AML, offering insights for researchers, practitioners, and industry stakeholders seeking to leverage this technology for enhanced data handling and interoperability in industrial automation.

Model-based systems engineering (MBSE) is gaining importance within the design of production systems. MBSE is exploited to start and support engineering-chain-crossing consistent and lossless engineering data transport and use finally resulting in a consistent information model that represent sets of digital twins for all relevant assets. Therefore, MBSE strongly depends on the modelling capabilities of the applied model rep-resentation technologies. One of these technologies currently under investigation is AutomationML. AutomationML has been proven to be efficient for information exchange and integration but has insufficient capabilities to represent dependencies be-tween asset properties. This paper introduces an Excel inspired dependency modelling approach for AutomationML based dig-ital twins, to take a step towards closing this gap.