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Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques

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Dynamics in Logistics (LDIC 2024)

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Abstract

Digital twins play an essential role in manufacturing companies to adopt Industry 4.0. However, their uptake has been lagging, especially in European manufacturing firms. This can be attributed to the absence of automated methods for digitizing physical manufacturing resources and creating digital representations accessible and processable by both humans and computers. Our research addresses this challenge by automating the digitization of manufacturing resources captured on the shop floor. We employ object detection techniques on a set of images and align the results with an ontology that standardizes the semantic description of digital representations. This research aims to accelerate digital transformation for manufacturing companies, providing digital representations to their physical resources. The ontology-based digital representation fosters interoperability among diverse equipment and machines from various vendors. It enables the automated deployment of digital twins, improving the efficiency of planning and control of manufacturing systems.

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References

  1. Falah, M.F., Sukaridhoto, S., Al Rasyid, M.U.H., Wicaksono, H.: Design of virtual engineering and digital twin platform as implementation of cyber-physical systems. Procedia Manuf. 52, 331–336 (2020)

    Google Scholar 

  2. Kritzinger, W., Karner, M., Traar, G., Henjes, J., Sihn, W.: Digital twin in manufacturing: a categorical literature review and classification. Ifac-PapersOnline 51(11), 1016–1022 (2018)

    Article  Google Scholar 

  3. Moder, P., Ehm, H., Jofer, E.: A holistic digital twin based on semantic web technologies to accelerate digitalization. In: Keil, S., Lasch, R., Lindner, F., Lohmer, J. (eds.) EADTC 2018-2019. LNEE, vol. 670, pp. 3–13. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-48602-0_1

    Chapter  Google Scholar 

  4. Lemaignan, S., Siadat, A., Dantan, J.-Y., Semenenko, A.: Mason: a proposal for an ontology of manufacturing domain. In: IEEE Workshop on Distributed Intelligent Systems: Collective Intelligence and Its Applications (DIS’06), pp. 195–200 (2006)

    Google Scholar 

  5. Wicaksono, H., Jost, F., Rogalski, S., Ovtcharova, J.: Energy efficiency evaluation in manufacturing through an ontology-represented knowledge base. Intell. Syst. Account. Financ. Manag. 21(1), 59–69 (2014)

    Article  Google Scholar 

  6. Wicaksono, H.: An integrated method for information and communication technology (ICT) supported energy efficiency evaluation and optimization in manufacturing: knowledge-based approach and energy performance indicators (EnPI) to support evaluation and optimization of energy efficiency. PhD thesis, Dissertation, Karlsruhe, Karlsruher Institut für Technologie (KIT), 2016 (2016)

    Google Scholar 

  7. de Roode, M., Fernández-Izquierdo, A., Daniele, L., Poveda-Villalón, M., García-Castro, R.: SAREF4INMA: a SAREF extension for the industry and manufacturing domain. Semant. Web 11(6), 911–926 (2020)

    Article  Google Scholar 

  8. Liu, S., Bao, J., Zheng, P.: A review of digital twin-driven machining: from digitization to intellectualization. J. Manuf. Syst. 67, 361–378 (2023)

    Article  Google Scholar 

  9. Alam, M.D., Kabir, G., Mirmohammadsadeghi, S.: A digital twin framework development for apparel manufacturing industry. Decis. Anal. J. 7, 100252 (2023)

    Google Scholar 

  10. Zhou, X., et al.: Intelligent small object detection for digital twin in smart manufacturing with industrial cyber-physical systems. IEEE Trans. Ind. Inform. 18(2), 1377–1386 (2021)

    Google Scholar 

  11. Sommer, M., Stjepandić, J., Stobrawa, S., von Soden, M.: Automated generation of digital twin for a built environment using scan and object detection as input for production planning. J. Ind. Inf. Integr. 33, 100462 (2023)

    Google Scholar 

  12. Denkena, B., Dittrich, M.A., Stobrawa, S., Stjepandic, J.: Automated generation of a digital twin using scan and object detection for data acquisition. Simulation in Produktion und Logistik, pp. 49–60 (2019)

    Google Scholar 

  13. Erkoyuncu, J.A., del Amo, I.F., Ariansyah, D., Bulka, D., Roy, R., et al.: A design framework for adaptive digital twins. CIRP Ann. 69(1), 145–148 (2020)

    Google Scholar 

  14. Xia, K., Saidy, C., Kirkpatrick, M., Anumbe, N., Sheth, A., Harik, R.: Towards semantic integration of machine vision systems to aid manufacturing event understanding. Sensors 21(13), 4276 (2021)

    Article  Google Scholar 

  15. Rozanec, J.M., et al.: Towards actionable cognitive digital twins for manufacturing. SeDiT@ ESWC 2615, pp. 1–12 (2020)

    Google Scholar 

  16. Meyer, M., Yu, Z., Gulati, P., Delforouzi, A., Roggenbuck, J., Wolf, K.: Ontologies for digital twins in smart manufacturing whitepaper

    Google Scholar 

  17. Psarommatis, F., Fraile, F., Ameri, F.: Zero defect manufacturing ontology: a preliminary version based on standardized terms. Comput. Ind. 145, 103832 (2023)

    Google Scholar 

  18. yolov5

    Google Scholar 

  19. Boris Sekachev, Nikita Manovich, Maxim Zhiltsov, Andrey Zhavoronkov, Dmitry Kalinin, Ben Hoff, TOsmanov, Dmitry Kruchinin, Artyom Zankevich, DmitriySidnev, Maksim Markelov, Johannes222, Mathis Chenuet, a andre, telenachos, Aleksandr Melnikov, Jijoong Kim, Liron Ilouz, Nikita Glazov, Priya4607, Rush Tehrani, Seungwon Jeong, Vladimir Skubriev, Sebastian Yonekura, vugia truong, zliang7, lizhming, and Tritin Truong. opencv/cvat: v1.1.0, August 2020

    Google Scholar 

  20. Bradski, G.: The OpenCV library. Dr. Dobb’s J. Softw. Tools (2000)

    Google Scholar 

  21. Buslaev, A., Iglovikov, V.I., Khvedchenya, E., Parinov, A., Druzhinin, M., Kalinin, A.A.: Albumentations: fast and flexible image augmentations. Information 11(2) (2020)

    Google Scholar 

  22. Glenn Jocher, Alex Stoken, Jirka Borovec, NanoCode012, ChristopherSTAN, Liu Changyu, Laughing, tkianai, Adam Hogan, lorenzomammana, yxNONG, AlexWang1900, Laurentiu Diaconu, Marc, wanghaoyang0106, ml5ah, Doug, Francisco Ingham, Frederik, Guilhen, Hatovix, Jake Poznanski, Jiacong Fang, Lijun Yu , changyu98, Mingyu Wang, Naman Gupta, Osama Akhtar, PetrDvoracek, and Prashant Rai. ultralytics/yolov5: v3.1 - Bug Fixes and Performance Improvements, October 2020

    Google Scholar 

  23. Fawcett, T.: An introduction to roc analysis. Pattern Recognit. Lett. 27(8), 861–874 (2006)

    Article  MathSciNet  Google Scholar 

  24. Martin, P., d’Acunto, A.: Design of a production system: an application of integration product-process. Int. J. Comput. Integr. Manuf. 16, 509–516 (2003)

    Google Scholar 

  25. Musen, M.A.: The protégé project: a look back and a look forward. AI Matters 1(4), 4–12 (2015)

    Article  Google Scholar 

  26. Nikita Sachdeva. Insights & blogs around software engineering - learn, develop, grow, 2023. https://insights.daffodilsw.com/blog/the-future-of-digital-twins

  27. Liu, J., Yu, D., Bi, X., Hu, Y., Yu, H., Li, B.: The research of ontology-based digital twin machine tool modeling. In: 2020 IEEE 6th International Conference on Computer and Communications (ICCC), pp. 2130–2134. IEEE (2020)

    Google Scholar 

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Authors and Affiliations

  1. School of Business, Social and Decision Sciences, Constructor University, Bremen, Germany

    Kritkorn Supyen, Abhishek Mathur, Tina Boroukhian & Hendro Wicaksono

Authors
  1. Kritkorn Supyen
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  2. Abhishek Mathur
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  3. Tina Boroukhian
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  4. Hendro Wicaksono
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Corresponding author

Correspondence to Hendro Wicaksono .

Editor information

Editors and Affiliations

  1. Production Systems and Logistics, University of Bremen, Bremen, Germany

    Michael Freitag

  2. Global Supply Chain Management, University of Bremen, Bremen, Germany

    Aseem Kinra

  3. Crowley Center for Transportation and Logistics, University of North Florida, Jacksonville, FL, USA

    Herbert Kotzab

  4. Combinatorial Optimization and Logistics, University of Bremen, Bremen, Germany

    Nicole Megow

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Supyen, K., Mathur, A., Boroukhian, T., Wicaksono, H. (2024). Streamlining Manufacturing Resource Digitization for Digital Twins Through Ontologies and Object Detection Techniques. In: Freitag, M., Kinra, A., Kotzab, H., Megow, N. (eds) Dynamics in Logistics. LDIC 2024. Lecture Notes in Logistics. Springer, Cham. https://doi.org/10.1007/978-3-031-56826-8_32

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  • DOI: https://doi.org/10.1007/978-3-031-56826-8_32

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  • Print ISBN: 978-3-031-56825-1

  • Online ISBN: 978-3-031-56826-8

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