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Predictive Maintenance for Cutter System of Roller Laminator

Author

Listed:
  • Ssu-Han Chen

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan
    Center for Artificial Intelligence & Data Science, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Chen-Wei Wang

    (Department of Industrial Engineering and Management, Ming Chi University of Technology, New Taipei City 243303, Taiwan)

  • Andres Philip Mayol

    (Manufacturing Engineering and Management Department, De La Salle University, Manila 0922, Philippines
    Center for Engineering and Sustainable Development Research, De La Salle University, Manila 0922, Philippines)

  • Chia-Ming Jan

    (Metal Industries Research & Development Centre, Kaohsiung City 811, Taiwan)

  • Tzu-Yi Yang

    (Department of Foreign Languages and Literature, National Ilan University, Ilan 260007, Taiwan)

Abstract

In the era of Industry 4.0, equipment maintenance is shifting toward data-driven strategies. Traditional methods rely on usage time or cycle counts to estimate component lifespan. This often causes early replacement of parts, leading to increased production costs. This study focuses on the cutter system of a roller laminator used in printed circuit board (PCB) manufacturing. An accelerometer is used to collect vibration signals under normal and abnormal states. Fast Fourier transform (FFT) is used to convert time-domain data into the frequency domain, then key statistical features from critical frequency bands are extracted as independent variables. The study applies logistic regression (LR), random forest (RF), and support vector machine (SVM) for predictive modeling of the cutting tool’s condition. The results show that the prediction accuracies of these models are 87.55%, 93.77%, and 94.94%, respectively, with SVM performing the best.

Suggested Citation

  • Ssu-Han Chen & Chen-Wei Wang & Andres Philip Mayol & Chia-Ming Jan & Tzu-Yi Yang, 2025. "Predictive Maintenance for Cutter System of Roller Laminator," Mathematics, MDPI, vol. 13(8), pages 1-17, April.
    • Handle: RePEc:gam:jmathe:v:13:y:2025:i:8:p:1264-:d:1632928
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    References listed on IDEAS

    as
    1. Shrinathan Esakimuthu Pandarakone & Yukio Mizuno & Hisahide Nakamura, 2019. "A Comparative Study between Machine Learning Algorithm and Artificial Intelligence Neural Network in Detecting Minor Bearing Fault of Induction Motors," Energies, MDPI, vol. 12(11), pages 1-14, June.
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