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Digital twin and predictive quality solution for insulated glass line

Author

Listed:
  • Gülcan Aydin

    (CMS Glass Machinery
    Kocaeli University)

  • Mehmet Tezcan

    (CMS Glass Machinery)

  • Bayram Ozgen

    (CMS Glass Machinery)

  • Tuğçe Nur Özkan

    (Simtera Informatics Communications Tech. Ltd. Co)

Abstract

This study is an integral part of an international research and development initiative investigating the application of digital twins and predictive quality solutions to enhance quality control and streamline production processes within the insulating glass manufacturing industry. The critical factor influencing the transformation of insulating glass into a high-quality, energy-efficient product is the gas filling rate. Therefore, this study focuses on the real-time monitoring and analysis of the gas filling process. Concurrently, predictive quality solutions are implemented to improve product quality and reduce defects. Consequently, it is evident that these technologies hold significant potential to advance the quality of insulating glass production and promote sustainable production practices on an international scale.

Suggested Citation

  • Gülcan Aydin & Mehmet Tezcan & Bayram Ozgen & Tuğçe Nur Özkan, 2025. "Digital twin and predictive quality solution for insulated glass line," Journal of Intelligent Manufacturing, Springer, vol. 36(5), pages 3543-3567, June.
    • Handle: RePEc:spr:joinma:v:36:y:2025:i:5:d:10.1007_s10845-024-02426-y
    DOI: 10.1007/s10845-024-02426-y
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    References listed on IDEAS

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    1. Kendrik Yan Hong Lim & Pai Zheng & Chun-Hsien Chen, 2020. "A state-of-the-art survey of Digital Twin: techniques, engineering product lifecycle management and business innovation perspectives," Journal of Intelligent Manufacturing, Springer, vol. 31(6), pages 1313-1337, August.
    2. Brock, Guy & Pihur, Vasyl & Datta, Susmita & Datta, Somnath, 2008. "clValid: An R Package for Cluster Validation," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 25(i04).
    3. Aniruddha Gaikwad & Reza Yavari & Mohammad Montazeri & Kevin Cole & Linkan Bian & Prahalada Rao, 2020. "Toward the digital twin of additive manufacturing: Integrating thermal simulations, sensing, and analytics to detect process faults," IISE Transactions, Taylor & Francis Journals, vol. 52(11), pages 1204-1217, November.
    4. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
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