The role of an ontology-based knowledge backbone in a circular factory
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Constantin Hofmann
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Steffen Staab
Abstract
In a circular factory, new products are produced reusing parts from used products, as well as newly manufactured parts. The production system consists of disassembly, testing as well as assembly steps. Due to the unforeseeable conditions of the used parts, the complexity of such a circular factory is challenging. This paper contributes a concept of an ontology-based knowledge backbone to master the challenges of such a circular factory. The concept addresses the representation of knowledge especially taking into account uncertainty, how to design queries and means to detect similarities and analogies. Furthermore, the role of research data management with automatized workflows as a supplier for FAIR data is elaborated.
Zusammenfassung
In einer Kreislauffabrik werden neue Produkte unter Wiederverwendung von Teilen gebrauchter Produkte sowie von neu hergestellten Teilen hergestellt. Das Produktionssystem besteht aus Demontage-, Prüf- und Montageschritten. Aufgrund des unvorhersehbaren Zustands der verwendeten Teile ist die Komplexität einer solchen Kreislauffabrik eine Herausforderung. In diesem Beitrag wird ein Konzept für ein ontologiebasiertes Wissensgerüst vorgestellt, um die Herausforderungen einer solchen Kreislauffabrik zu meistern. Das Konzept befasst sich mit der Repräsentation von Wissen unter besonderer Berücksichtigung von Unsicherheit, der Gestaltung von Abfragen und der Erkennung von Ähnlichkeiten und Analogien. Darüber hinaus wird die Rolle des Forschungsdatenmanagements mit automatisierten Workflows als Lieferant für FAIR-Daten herausgearbeitet.
About the authors
Constantin Hofmann is a researcher at the Institute for Production Science (wbk) at the Karlsruhe Institute of Technology (KIT). His research concerns production system optimisation on strategical, tactical and operational levels by means of intelligent algorithms.
Steffen Staab is a Professor of Analytic Computing at the University of Stuttgart and holds a Chair for Web and Computer Science at the University of Southampton. His research interests are related to making sense of data and knowledge by acquiring, representing, and reasoning with its semantics including methods of machine learning as well as formal logics.
Michael Selzer is group leader of the cross-sectional platform for research data management at the Institute of Nanotechnology (INT) at the Karlsruhe Institute of Technology (KIT). He has a strong background in research software engineering (RSE) of large software frameworks. In recent years, his interest has shifted to research data management and he is the founder of the Karlsruhe data infrastructure (for materials science) Kadi4Mat.
Gerhard Neumann is Full Professor and Chair of the Autonomous Learning Robots Lab at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology (KIT). His research is focused on the intersection of machine learning, robotics, and human-robot interaction.
Kai Furmans is currently a professor in mechanical engineering and the Head of the Institute for Material Handling and Logistics (IFL) of the Karlsruhe Institute of Technology (KIT). His research interests include automation and robotics in material handling and modeling of such systems.
Michael Heizmann is Professor of Mechatronic Measurement Systems at the Institute of Industrial Information Technology at the Karlsruhe Institute of Technology (KIT). His research interests include measurement science, machine vision, signal and image processing, image and information fusion and their applications.
Jürgen Beyerer has been a full professor for informatics at the Institute for Anthropomatics and Robotics at the Karlsruhe Institute of Technology (KIT) since March 2004 and director of the Fraunhofer Institute of Optronics, System Technologies and Image Exploitation IOSB in Ettlingen, Karlsruhe, Ilmenau, Görlitz, Lemgo, Oberkochen and Rostock. Research interests include automated visual inspection, signal and image processing, variable image acquisition and processing, active vision, metrology, information theory, fusion of data and information from heterogeneous sources, system theory, autonomous systems and automation.
Gisela Lanza is member of the management board at the Institute of Production Science (wbk) of the Karlsruhe Institute of Technology (KIT). She heads the Production Systems division dealing with the topics of global production strategies, production system planning, and quality assurance in research and industrial practice. Her research focus is on the holistic design and evaluation of production systems. The methodological approach includes the use of quantitative methods to increase efficiency. In addition, a special focus is placed on data-driven planning and control of production networks in order to translate corporate strategy into tactical and operative network design.
Julius Pfrommer is an industrial engineer and received his doctorate from the Faculty of Computer Science at the Karlsruhe Institute of Technology (KIT) on algorithmic issues of decentralized planning. In his current role as group leader at the Fraunhofer IOSB, he works on the optimization of industrial processes using AI and machine learning methods, as well as information management in production. Dr. Pfrommer is also the scientific director of the Competence Center for AI Engineering (CC-KING) in Karlsruhe.
Tobias Düser is the head of the IPEK - Institute of Product Engineering at the Karlsruhe Institute of Technology (KIT). His research focuses on System Generation Engineering, X-in-the-loop methods for the validation of systems-of-systems, advanced systems engineering methods in product development and validation, methods and processes for the virtual validation of highly automated vehicles with a special focus on environment, infrastructure, humans and sensors, trustworthiness and credibility of cyber-physical and virtual validation methods, metaverse-based approaches and update and upgrade capability of software-driven, mechatronic products.
Jan-Felix Klein is a researcher at the Institute for Material Handling and Logistics (IFL) at the Karlsruhe Institute of Technology (KIT). His research concerns digital twins in the context of material handling in smart remanufacturing systems.
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Research ethics: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
References
[1] D. Lin, L. Wambersie, and M. Wackernagel, “Estimating the date of earth overshoot day 2022,” in Nowcasting the World’s Footprint & Biocapacity for 2021, 2021, pp. 1–8.Suche in Google Scholar
[2] IEA, “Global CO2 emissions by sector, 2019–2022,” Paris, IEA, 2022. Available at: https://www.iea.org/data-and-statistics/charts/global-co2-emissions-by-secto r-2019-2022.Suche in Google Scholar
[3] A. Benoy, L. Owen, and M. Folkerson, “Triple win-the social, economic and environmental case for remanufacturing,” in All-Party Parliamentary Sustainable Resource Group & All-Party Parliamentary Manufacturing Group, London, 2014.Suche in Google Scholar
[4] M. Yahya, J. G. Breslin, and M. I. Ali, “Semantic web and knowledge graphs for industry 4.0,” Appl. Sci., vol. 11, no. 11, p. 5110, 2021. https://doi.org/10.3390/app11115110.Suche in Google Scholar
[5] I. Grangel-González, F. Lösch, and A. ul Mehdi, “Knowledge graphs for efficient integration and access of manufacturing data,” in 2020 25th IEEE International Conference on Emerging Technologies and Factory Automation (ETFA), vol. 1, 2020, pp. 93–100.10.1109/ETFA46521.2020.9212156Suche in Google Scholar
[6] J. Wan, B. Yin, D. Li, A. Celesti, F. Tao, and Q. Hua, “An ontology-based resource reconfiguration method for manufacturing cyber-physical systems,” IEEE ASME Trans. Mechatron., vol. 23, no. 6, pp. 2537–2546, 2018. https://doi.org/10.1109/tmech.2018.2814784.Suche in Google Scholar
[7] D. Lünsch, P. Detzner, A. Ebner, and S. Kerner, “Swap-it: a scalable and lightweight industry 4.0 architecture for cyber-physical production systems,” in 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), IEEE, 2022, pp. 312–318.10.1109/CASE49997.2022.9926665Suche in Google Scholar
[8] S. Staab and R. Studer, Handbook on Ontologies, Heidelberg: London: New York, Springer Science & Business Media, 2010.10.1007/978-3-540-92673-3Suche in Google Scholar
[9] L. Griem, et al., “Kadistudio: fair modelling of scientific research processes,” Data Sci. J., vol. 21, no. 1, p. 16, 2022. https://doi.org/10.5334/dsj-2022-016.Suche in Google Scholar
[10] J. Pfrommer, et al.., “An ontology for remanufacturing systems,” Automatisierungstechnik, vol. 70, no. 6, pp. 534–541, 2022. https://doi.org/10.1515/auto-2021-0156.Suche in Google Scholar
[11] R. Arndt, R. Troncy, S. Staab, L. Hardman, and M. Vacura, “COMM: designing a well-founded multimedia ontology for the web,” in The Semantic Web: 6th International Semantic Web Conference, 2nd Asian Semantic Web Conference, ISWC 2007+ ASWC 2007, Busan, Korea, November 11–15, 2007. Proceedings, Springer, 2007, pp. 30–43.10.1007/978-3-540-76298-0_3Suche in Google Scholar
[12] A. Haller, et al.., “The modular SSN ontology: a joint W3C and OGC standard specifying the semantics of sensors, observations, sampling, and actuation,” Semantic Web, vol. 10, no. 1, pp. 9–32, 2019. https://doi.org/10.3233/sw-180320.Suche in Google Scholar
[13] International Organization for Standardization, Uncertainty of Measurement-Part 3: Guide to the Expression of Uncertainty in Measurement (GUM: 1995), Geneva, Switzerland, ISO, 2008.Suche in Google Scholar
[14] G. Gaur, A. Dang, A. Bhattacharya, and S. Bedathur, “Computing and maintaining provenance of query result probabilities in uncertain knowledge graphs,” in Proceedings of the 30th ACM International Conference on Information & Knowledge Management, 2021, pp. 545–554.10.1145/3459637.3482330Suche in Google Scholar
[15] T. Lukasiewicz and U. Straccia, “Managing uncertainty and vagueness in description logics for the semantic web,” J. Web Semant., vol. 6, no. 4, pp. 291–308, 2008. https://doi.org/10.1016/j.websem.2008.04.001.Suche in Google Scholar
[16] X. Lian and L. Chen, “Efficient query answering in probabilistic rdf graphs,” in Proceedings of the 2011 ACM SIGMOD International Conference on Management of data, 2011, pp. 157–168.10.1145/1989323.1989341Suche in Google Scholar
[17] R. Peñaloza and N. Potyka, “Towards statistical reasoning in description logics over finite domains,” in International Conference on Scalable Uncertainty Management, Springer, 2017, pp. 280–294.10.1007/978-3-319-67582-4_20Suche in Google Scholar
[18] B. Xiong, N. Potyka, T.-K. Tran, M. Nayyeri, and S. Staab, “Faithful embeddings for EL++ knowledge bases,” in International Semantic Web Conference, Springer, 2022, pp. 22–38.10.1007/978-3-031-19433-7_2Suche in Google Scholar
[19] V. Belle and L. De Raedt, “Semiring programming: a semantic framework for generalized sum product problems,” Int. J. Approx. Reason., vol. 126, pp. 181–201, 2020, https://doi.org/10.1016/j.ijar.2020.08.001.Suche in Google Scholar
[20] D. Hernández, L. Galárraga, and K. Hose, “Computing how-provenance for sparql queries via query rewriting,” Proc. VLDB Endow., vol. 14, no. 13, pp. 3389–3401, 2021. https://doi.org/10.14778/3484224.3484235.Suche in Google Scholar
[21] A. Bordes, N. Usunier, A. Garcia-Duran, J. Weston, and O. Yakhnenko, “Translating embeddings for modeling multi-relational data,” Adv. Neural Inf. Process. Syst., vol. 26, 2013.Suche in Google Scholar
[22] G. A. Gesese, R. Biswas, M. Alam, and H. Sack, “A survey on knowledge graph embeddings with literals: which model links better literal-ly?” Semantic Web, vol. 12, no. 4, pp. 617–647, 2021. https://doi.org/10.3233/sw-200404.Suche in Google Scholar
[23] M. Schlichtkrull, T. N. Kipf, P. Bloem, R. Van Den Berg, I. Titov, and M. Welling, “Modeling relational data with graph convolutional networks,” in The Semantic Web: 15th International Conference, ESWC 2018, Heraklion, Crete, Greece, June 3–7, 2018, Proceedings 15, Springer, 2018, pp. 593–607.10.1007/978-3-319-93417-4_38Suche in Google Scholar
[24] T. Monninger, et al.., “Scene: reasoning about traffic scenes using heterogeneous graph neural networks,” IEEE Rob. Autom. Lett., vol. 8, no. 3, pp. 1531–1538, 2023. https://doi.org/10.1109/lra.2023.3234771.Suche in Google Scholar
[25] N. Brandt, et al., “Kadi4Mat: a research data infrastructure for materials science,” Data Sci. J., vol. 20, no. 1, 2021. https://doi.org/10.5334/dsj-2021-008.Suche in Google Scholar
[26] R. Al-Salman, et al., “Kadistudio use-case workflow: automation of data processing for in situ micropillar compression tests,” Data Sci. J., vol. 22, no. 21, 2023. https://doi.org/10.5334/dsj-2023-021.Suche in Google Scholar
[27] A. Koeppe, F. Bamer, M. Selzer, B. Nestler, and B. Markert, “Workflow concepts to model nonlinear mechanics with computational intelligence,” PAMM, vol. 21, no. 1, p. e202100238, 2021. https://doi.org/10.1002/pamm.202100238.Suche in Google Scholar
© 2024 Walter de Gruyter GmbH, Berlin/Boston
Artikel in diesem Heft
- Frontmatter
- Editorial
- Perpetual innovative products from circular factories for sustainable production
- Survey
- The vision of the circular factory for the perpetual innovative product
- Long living human-machine systems in construction and production enabled by digital twins
- Methods
- Enabling the vision of a perpetual innovative product – predicting function fulfillment of new product generations in a circular factory
- Managing uncertainty in product and process design for the circular factory
- Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory
- Self-learning and autonomously adapting manufacturing equipment for the circular factory
- The role of an ontology-based knowledge backbone in a circular factory
- Analysis and evaluation of adaptive remanufacturing strategies for mechanical products
Artikel in diesem Heft
- Frontmatter
- Editorial
- Perpetual innovative products from circular factories for sustainable production
- Survey
- The vision of the circular factory for the perpetual innovative product
- Long living human-machine systems in construction and production enabled by digital twins
- Methods
- Enabling the vision of a perpetual innovative product – predicting function fulfillment of new product generations in a circular factory
- Managing uncertainty in product and process design for the circular factory
- Learning human actions from complex manipulation tasks and their transfer to robots in the circular factory
- Self-learning and autonomously adapting manufacturing equipment for the circular factory
- The role of an ontology-based knowledge backbone in a circular factory
- Analysis and evaluation of adaptive remanufacturing strategies for mechanical products
