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6th International Conference on Industry 4.0 and Smart Manufacturing, ISM 2024

作者： Manoury, Marvin Riedelsheimer, Theresa Hellmeier, Malte Meyer, Tom Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrasse 8-9 Berlin10587 Germany Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraße 6 Dortmund44147 Germany

Considering the concept of a Circular Economy, which entails several life cycles of, e.g., vehicles, their components, and materials, it is important to investigate how the respective Digital Twins are managed over the lifecycle of their physical assets. This publication presents and compares three approaches for managing Digital Twins in industrial use cases. For the aggregation of these approaches, a user-centered design approach has been used in the research project Catena-X which focuses on the automotive industry. The results are analyzed qualitatively based on aspects such as data ownership and data sovereignty. It was identified, that all three approaches have limitations and benefits which make them useful in different scenarios. An overview of their strengths and weaknesses is presented to select an appropriate approach In similar setups. The results described in this publication can be used to select the best update approach in data spaces where multiple stakeholders are able to update information on a single asset. © 2025 The Authors.

关键词： Supply chains

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Extended Abstract of Poster: STARS: Tree-Based Classification and Testing of Feature Combinations in the Automated Robotic Domain 18

Extended Abstract of Poster: STARS: Tree-Based Classificatio...

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18th IEEE International Conference on software Testing, Verification and Validation Workshops, ICSTW 2025

作者： Schallau, Till Schmid, Dominik Pawlinorz, Nick Naujokat, Stefan Howar, Falk TU Dortmund University Dortmund44227 Germany Fraunhofer Institute for Software and Systems Engineering Dortmund44147 Germany

ISBN: (纸本)9798331534677

Testing complex systems is crucial for ensuring safety, especially in automated driving, where diverse data sources and variable environments pose challenges. Here, robust safety validation is critical but exhaustive n-way combinatorial testing is impractical due to the vast number of test cases. The STARS framework uses tree-based scenario classifiers to limit feature combinations in a given domain. © 2025 IEEE.

关键词： Automation

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Supporting Changes in Digital Ownership and Data Sovereignty Across the Automotive Value Chain with Catena-X

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Procedia Computer Science 2025年 253卷 374-383页

作者： Marvin Manoury Theresa Riedelsheimer Malte Hellmeier Tom Meyer Fraunhofer Institute for Production Systems and Design Technology IPK Pascalstrasse 8-9 10587 Berlin Germany Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraße 6 44147 Dortmund Germany

关键词： Digital Twins Ownership Circular Economy Data Sovereignty Supply Chain Catena-X

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Extended Abstract of Poster: STARS: Tree-Based Classification and Testing of Feature Combinations in the Automated Robotic Domain

Extended Abstract of Poster: STARS: Tree-Based Classificatio...

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IEEE International Conference on software Testing Verification and Validation Workshop, ICSTW

作者： Till Schallau Dominik Schmid Nick Pawlinorz Stefan Naujokat Falk Howar TU Dortmund University Dortmund Germany Fraunhofer Institute for Software and Systems Engineering Dortmund Germany

ISBN: (数字)9798331534677

ISBN: (纸本)9798331534684

关键词： Combinatorial testing Soft sensors Conferences Stars Focusing Safety Complexity theory Complex systems Robots Monitoring

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Pattern-based Requirements Elicitation for Sovereign Data Sharing

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Procedia Computer Science 2025年 254卷 147-156页

作者： Julia Pampus Maritta Heisel Fraunhofer Institute for Software and Systems Engineering ISST Speicherstraβe 6 44147 Dortmund Germany University of Duisburg-Essen Bismarckstr 90 47057 Duisburg Germany

The growing data economy increasingly focuses on self-determined and autonomous data sharing, supported by infrastructures that create a trustful and secure environment. A key feature in this context is the offering, negotiation, and enforcement of so-called data usage conditions (DUCs), also known as policies. The design of involved software systems requires a structured and stakeholder-specific elicitation of technical requirements. To address this issue, we define a requirements model, consisting of actor, dataset, and condition entities, for sovereign data sharing and present a method for requirements elicitation in form of a five-step agenda with 13 validation conditions (VCs). The application of this method produces a set of instantiated requirements templates that provide descriptive information about involved actors, identified datasets, and applied DUCs. We demonstrate our method using an established use case from the automotive industry and evaluate it in qualitative expert interviews.

关键词： Data Sharing Sovereignty Data Usage Conditions Data Spaces Patterns Requirements engineering

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Combining Simulation and Recurrent Neural Networks for Model-Based Condition Monitoring of Machines 24

Combining Simulation and Recurrent Neural Networks for Model...

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Proceedings of the Winter Simulation Conference

作者： Alexander Wuttke Markus Rabe Joachim Hunker Jan-Philipp Diepenbrock Dept. IT in Production and Logistics TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering Dortmund GERMANY IVA Schmetz GmbH Menden GERMANY

ISBN: (纸本)9798331534202

Maintenance is pivotal in industry, with condition-based maintenance emerging as a key strategy. This involves monitoring the machine condition through sensor data analysis. Model-based approaches compare observed data with expected values from models, which requires high-quality models. An established method is to use simulation models, which in many cases produce good results but may lack precision due to uncertainties. Alternatively, models created by machine learning can detect patterns directly from data. This paper proposes combining simulation models with machine learning models, leveraging the simulation's a-priori knowledge and machine learning's data patterns to enhance models for condition monitoring. Recurrent neural networks are suggested as the machine learning method. The paper outlines a systematic approach and demonstrates its application in an industrial use case, which investigates vacuum processes in industrial furnaces.

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Potentials and Barriers of the Metaverse for Circular Economy 24

Potentials and Barriers of the Metaverse for Circular Econom...

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Proceedings of the Winter Simulation Conference

作者： Julia Kunert Hendrik van der Valk Hannah Scheerer Christoph Hoppe Chair for Industrial Inform. Management TU Dortmund University Dortmund NRW GERMANY Chair for Industrial Inform. Management TU Dortmund University Dortmund NRW GERMANY and Fraunhofer Institute for Software and Systems Engineering ISST Dortmund NRW GERMANY Dept. of Industrial Manufacturing Fraunhofer ISST Dortmund NRW GERMANY

ISBN: (纸本)9798331534202

Sustainability is a challenge for society that circular economy tries to tackle. The metaverse, as an emerging technology that incorporates digital twins and simulation in an immersive virtual environment, has not been thoroughly investigated in connection to circular economy. Thus, the purpose of this study is to summarize the potentials and barriers of the use of the metaverse for circular economy. By conducting a structured literature review, this paper categorizes the findings into dimensions that are important for both the metaverse and circular economy. A variety of potentials and barriers that cover different perspectives important for businesses aiming to comply with circular economy principles is discovered. The findings include potentials and barriers in several areas, like the access to the metaverse, connected costs, data, knowledge transfer, collaboration, innovation, product design, production planning, training of employees, and transportation. The results can be used to promote the implementation of circular economy principles.

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Industrial Metaverse in Supply Chain Management: Applications, Concepts, and Open Research Paths 24

Industrial Metaverse in Supply Chain Management: Application...

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Proceedings of the Winter Simulation Conference

作者： Hendrik van der Valk Julia Kunert Niklas Harke Katharina Langenbach Chair for Industrial Information Management TU Dortmund University Dortmund NRW GERMANY and Fraunhofer Institute for Software and Systems Engineering ISST Dortmund NRW GERMANY Chair for Industrial Information Management TU Dortmund University Dortmund NRW GERMANY Dept. of IT in Production and Logistics TU Dortmund University Dortmund NRW GERMANY

ISBN: (纸本)9798331534202

This research study examines the applications of the industrial metaverse in supply chain management. Thus, it reviews and analyzes the up-to-date knowledge along the Supply Chain Operations Reference Model. Therefore, a structured literature review is conducted. We derive six core functionalities of the industrial metaverse in supply chains: visibility and monitoring, prediction, simulation, collaboration, training, and optimization. Furthermore, their presence along the different phases of a supply chain is investigated. Besides the functionalities, the relationship between the industrial metaverse and simulation applications and digital twins is analyzed, and a brief description of possible metaverse architectures is provided. This study additionally derives research gaps within the emerging field of metaverse applications and research and defines paths to tackle them.

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Enabling Data-Driven Nursing Innovations: User-centered Development of a Nursing Data Module

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Current Directions in Biomedical engineering 2023年第1期9卷 339-342页

作者： Warnecke, Maren Holle, Daniela Burmann, Anja Fraunhofer Institute for Software and Systems Engineering ISST Speicherstrasse 6 Dortmund Germany HS Gesundheit Bochum Bochum Germany Fraunhofer Institute for Software and Systems Engineering ISST Dortmund Germany

The documentation landscape for nursing care data in Germany is predominantly heterogeneous and unstructured. Therefore, insightful methods such as Artificial Intelligence (AI) are difficult to implement. We propose a stepwiseapproach that identifies relevant information from nursing theory and practice and maps it to a standardized nursing core data set to enable data-based improvement of nursing care. This can be used for various use-cases in care, such as risk detection and prevention in diverse care contexts. Many care processes can benefit of a cross-facility and standardized repository and associated applications. We propose an approach that enables the use of AI while leveraging consensus and evidenced-based expert knowledge from nursing science. © 2023 the author(s), published by Walter de Gruyter Berlin/Boston.

关键词： Nursing

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Evaluating the effectiveness of systems and software engineering methods, processes and tools for use in defense programs

Evaluating the effectiveness of systems and software enginee...

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Annual IEEE systems Conference

作者： Michael Pennotti Richard Turner Forrest Shull School of Systems and Enterprises Stevens Institute of Technology Hoboken NJ USA Fraunhofer Center of Experimental Software Engineering University of Maryland College Park MD USA

The systems engineering research center university affiliated research center (SERC-UARC) at Stevens institute has been tasked to evaluate the effectiveness of the systems and software engineering processes, methods and tools (MPTs) used in US department of defense acquisition and development programs. This paper presents the selection and evaluation process, describes its evolution based on changing sponsor needs, and presents additional information on the characterization of MPTs for evaluation.

关键词： software engineering systems engineering and theory USA Councils Best practices Maintenance engineering Design engineering Educational institutions Costs Engines Weapons

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