Predictive Maintenance for Cutter System of Roller Laminator

Ssu-Han Chen, Chen-Wei Wang (), Andres Philip Mayol, Chia-Ming Jan and Tzu-Yi Yang ()
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Ssu-Han Chen: Department of Industrial Engineering and Management, 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
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

Mathematics, 2025, vol. 13, issue 8, 1-17

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.

Keywords: predictive maintenance; fast Fourier transform; machine learning; roller laminator (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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