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Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing

Author

Listed:
  • Jonghyuk Kim

    (Division of Computer Science and Engineering, Sunmoon University, 70, Sunmoon-ro 221 beon-gil, Tangjeong-myeon, Asan-si 31460, Korea)

  • Hyunwoo Hwangbo

    (Graduate School of Information, Yonsei University, 50, Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

In this study, real-time preventive measures were formulated for a crusher process that is impossible to automate, due to the impossibility of installing sensors during the production of plastic films, and a real-time early warning system for semi-automated processes subsequently developed. First, the flow of a typical film process was ascertained. Second, a sustainable plan for real-time forecasting in a process that cannot be automated was developed using the semi-automation method flexible structure production control (FSPC). Third, statistical early selection of the process variables that are most probably responsible for failure was performed during data preprocessing. Then, a new, unified dataset was created using the link reordering method to transform the time sequence of the continuous process into one time zone. Fourth, a sustainable prediction algorithm was developed using the association rule method along with traditional statistical techniques, and verified using actual data. Finally, the overall developed logic was applied to new production process data to verify its prediction accuracy. The developed real-time early warning system for semi-automated processes contributes significantly to the smart manufacturing process both theoretically and practically.

Suggested Citation

  • Jonghyuk Kim & Hyunwoo Hwangbo, 2019. "Real-Time Early Warning System for Sustainable and Intelligent Plastic Film Manufacturing," Sustainability, MDPI, vol. 11(5), pages 1-13, March.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:5:p:1490-:d:213005
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    References listed on IDEAS

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    Cited by:

    1. Muawia Ramadan & Bashir Salah & Mohammed Othman & Arsath Abbasali Ayubali, 2020. "Industry 4.0-Based Real-Time Scheduling and Dispatching in Lean Manufacturing Systems," Sustainability, MDPI, vol. 12(6), pages 1-18, March.
    2. Elsa Chaerun Nisa & Yean-Der Kuan, 2021. "Comparative Assessment to Predict and Forecast Water-Cooled Chiller Power Consumption Using Machine Learning and Deep Learning Algorithms," Sustainability, MDPI, vol. 13(2), pages 1-18, January.

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