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Approaches And Research Directions For Adapting Rapid Prototyping In Industrial Service Development: A Systematic Literature Review

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Procedia CIRP 2024年 130卷 562-572页

作者： Daniel Hefft Nick Große Alexander Kreyenborg David Kiklhorn Michael Henke Fraunhofer Institute for Material Flow and Logistics Joseph-von-Fraunhofer-Str. 2-4 44227 Dortmund Germany TU Dortmund University August-Schmidt-Str. 4 44227 Dortmund Germany Fraunhofer Institute for Software and Systems Engineering Speicherstr. 6 44147 Dortmund Germany

Services offer companies unique opportunities to expand their portfolio with customer-oriented solutions leading to competitive advantages due to their uniqueness. Nevertheless, companies struggle with the development and successful implementation of industrial services. The main reason for this lies in a lack of suitable development approaches and procedures for industrial services, which primarily originate from product development. Rapid prototyping (RP) aims to enhance service development (SD) by considering the need for highly iterative and fast development with simultaneous involvement of partners and customers. Against that backdrop, this paper explores use-cases, frameworks, and applications of RP to contribute to restructuring industrial SD. Utilizing a systematic literature review (SLR), RP approaches in the various disciplines of engineering are collected. After extracting important features and aggregated dimensions, the authors compare their findings to meta-theoretical foundations of Service-Dominant Logic (SDL) to give an outlook on future research on adapting RP in SD.

关键词： Rapid Prototyping Service Development Industrial Services Service-Dominant Logic Systematic Literature Review

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Reliability Estimation of ML for Image Perception: A Lightweight Nonlinear Transformation Approach Based on Full Reference Image Quality Metrics

Reliability Estimation of ML for Image Perception: A Lightwe...

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IEEE International Symposium on Embedded Multicore Socs (MCSoC)

作者： João-Vitor Zacchi Francesco Carella Priyank Upadhya Shanza Ali Zafar John Molloy Lisa Jöckel Janek Groß Núria Mata Nguyen Anh Vu Doan Fraunhofer Institute for Cognitive Systems IKS Munich Germany University of York York United Kingdom Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany

As machine learning (ML) models for image perception continue to advance, ensuring their robustness and reliability under various real-world scenarios remains a significant challenge. Image quality factors, such as blur, brightness, and other environmental conditions, can significantly affect the performance of these algorithms, leading to inaccurate detection and potential failures in critical applications. In this paper, we propose a comprehensive diagnosis framework that leverages image quality metrics to assess and enhance the performance of these algorithms. To accomplish this goal, we deliberately introduce disturbances in parameters such as brightness, saturation, and other relevant factors. Subsequently, we compute a set of full-reference image quality metrics to evaluate the image quality after the perturbations. Once we have obtained the metrics, we apply a nonlinear transformation to these values. Based on the transformed metrics, we create a regression model that predicts the detection Intersection over Union (IOU). To validate our framework, we conducted experiments using three state-of-the-art machine learning models for object detection and instance segmentation. The models were subjected to various scenarios with different levels of image quality perturbations. Our experimental results clearly demonstrate the possibility of establishing a strong correlation between image quality metrics and the performance of the algorithms.

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Digital twins in simulative applications: a taxonomy 20

Digital twins in simulative applications: a taxonomy

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Proceedings of the Winter Simulation Conference

作者： Hendrik van der Valk Joachim Hunker Markus Rabe Boris Otto TU Dortmund University Dortmund GERMANY Fraunhofer Institute for Software and Systems Engineering ISST Dortmund GERMANY

ISBN: (纸本)9781728194998

With the advances in information technology, the concept of Digital Twins has gained wide attention in both practice and research in recent years. A Digital Twin is a virtual representation of a physical object or system and is connected in a bi-directional way with the physical counterpart. The aim of a Digital Twin is to support all stakeholders during the whole lifecycle of such system or object. One of the core aspects of a Digital Twin is modeling and simulation, which is a well-established process, e.g., in the development of systems. Simulation models can be distinguished on the basis of different dimensions, e.g., on the basis of their time perspective. The existing literature reviews have paid little to no attention to this simulation aspect of a Digital Twin. In order to address this, the authors have developed a taxonomy based on an extended literature review to bridge the aforementioned gap.

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

Combining Simulation and Recurrent Neural Networks for Model...

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Simulation Winter Conference

ISBN: (数字)9798331534202

ISBN: (纸本)9798331534219

关键词： Condition monitoring Industries Recurrent neural networks Vacuum systems Uncertainty Systematics Machine learning Data models Maintenance Monitoring

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Exploring the Abstraction Levels of Design Principles: The Case of Chatbots 17

Exploring the Abstraction Levels of Design Principles: The C...

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17th International Conference on Wirtschaftsinformatik, WI 2022

作者： Wache, Hendrik Möller, Frederik Schoormann, Thorsten Strobel, Gero Petrik, Dimitri Chemnitz University of Technology Business Information Systems – Business Process and Information Management Chemnitz Germany TU Dortmund University Industrial Information Man Germany Fraunhofer ISST Dortmund Germany University of Hildesheim Enterprise Modeling and Information Syst. Hildesheim Germany University Duisburg-Essen Business Informatics and Software Engineering Essen Germany University of Stuttgart Information Systems II Stuttgart Germany

Formulating design principles is the primary mechanism to codify design knowledge which elevates its meaning to a general level and applicability. Although we can observe a great variety of abstraction levels in available design principles, spanning from more situated to more generic levels, there is only limited knowledge about the corresponding (dis-)advantages of using a certain level of abstraction. That is problematic because it hinders researchers in making informed decisions regarding the (intended) level of abstraction and practitioners in being oriented whether the principles are already contextualized or still require effort to apply them within their situation. Against this backdrop, this paper (1) explores different abstraction levels based on a sample of 69 design principles from the chatbot domain as well as (2) provides a preliminary positioning framework and lessons learned. We aim to complement methodological guidance and strengthen the principles' applicability, leading to knowledge reuse. © 2022 17th International Conference on Wirtschaftsinformatik, WI 2022. All rights reserved.

关键词： Design Principles Design Science Prescriptive Design Knowledge

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Data spaces as enablers for sustainability

Data spaces as enablers for sustainability

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2023 Designing Futures: Zukunfte gestalten, INFORMATIK 2023 - 2023 Designing Futures: Shaping the Future, INFORMATIK 2023

作者： Hoppe, Christoph Schmelzer, Robert Möller, Frederik Schoormann, Thorsten Fraunhofer Institute for Software and Systems Engineering Industrial Manufacturing Speicherstraße 6 Dortmund44147 Germany Technische Universität Chemnitz Business Informatics Straße der Nationen 62 Chemnitz09111 Germany Technische Universität Braunschweig Institute for Business Informatics Rebenring 58a Braunschweig38106 Germany Universität Hildesheim Department for Information Systems Samelsonplatz Hildesheim31141 Germany

ISBN: (纸本)9783885797319

One of our society's most fundamental challenges is promoting sustainable development. Data sharing across organizations is one way to spur innovation and address the Sustainable Development Goals (SDGs) through optimizing resource utilization, fostering circular supply chains, and producing accurate information about CO2 emissions. However, organizations often hesitate to share data given a range of concerns, including the fear of data misappropriation or the lack of control over what others do with their data. Data spaces as a novel artifact seek to tackle these issues by providing an integrated data management approach upholding data sovereignty. These spaces can boost sustainable development by being a secure and trusted digital infrastructure for organizations to share data for sustainable purposes. To disclose how this digital solution fosters sustainability, we present a set of 65 potentials of data spaces along with the dimensions of ecological, economic, and social sustainability. © 2023 Gesellschaft fur Informatik (GI). All rights reserved.

关键词： Sustainable development

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Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

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IEEE International Workshop on Metrology for Agriculture and Forestry (MetroAgriFor)

作者： Stefan Rilling Jens Henningsen Katharina Milde Daniel Martini Lorenz Wickert Patricia Kelbert Adaptive Reflective Teams Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS Sankt Augustin Germany Data Science Fraunhofer Institute for Experimental Software Engineering IESE Kaiserslautern Germany Team Digital Technologies Advisory Board for Technology and Construction in Agriculture KTBL Darmstadt Germany

ISBN: (数字)9798350355444

ISBN: (纸本)9798350355451

This article looks at the sustainability of various artificial intelligence (AI) methods. In recent months, AI methods have received a great deal of public attention and the benefits for sustainability have been emphasised. However, the ecological impacts of AI methods must also be considered in this debate. This article presents an overview of various approaches to sustainability assessment and addresses issues relating to energy consumption and the associated Carbon dioxide (CO2) emissions, and a Life Cycle Assessment (LCA) calculation for a concrete agricultural AI-use case.

关键词： Life cycle assessment Energy consumption Conferences Forestry Carbon dioxide Metrology Agriculture Artificial intelligence Sustainable development Carbon emissions

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Overview and Assessment Approaches of Energy Consumption and GHG Emissions Through the Development and Application of Artificial Intelligence - An Example from the Agricultural Sector

Overview and Assessment Approaches of Energy Consumption and...

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2024 IEEE International Workshop on Metrology for Agriculture and Forestry, MetroAgriFor 2024

作者： Rilling, Stefan Henningsen, Jens Milde, Katharina Martini, Daniel Wickert, Lorenz Kelbert, Patricia Fraunhofer Institute for Intelligent Analysis and Information Systems Iais Adaptive Reflective Teams Sankt Augustin Germany Fraunhofer Institute for Experimental Software Engineering Iese Data Science Kaiserslautern Germany Advisory Board for Technology and Construction in Agriculture Ktbl Team Digital Technologies Darmstadt Germany

ISBN: (纸本)9798350355444

关键词： Crops

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