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Intelligent Diagnostics and Performance Optimization of Machine Tool Through Integrated Vibration and Modal Assessment for Industry 4.0
Aman Ullah a, Tzu-Chi Chan b and Zhong-yan Xie b
aDepartment of Power Mechanical Engineering, National Formosa University, Yunlin, Taiwan, R.O.C
bDepartment of Mechanical and Computer-Aided Engineering, National Formosa University, Yunlin, Taiwan, R.O.C.
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Abstract:
Precision machining is often suffers by dynamic errors, vibrations, and thermal distortions that detrimentally affect accuracy and increase machining time. To address these issues, the present study proposes a systematic methodology integrating Finite Element Analysis (FEA), Experimental Modal Analysis (EMA), and a real-time Prediction Diagnosis Performance System (PDPS) to evaluate dynamic behavior and compensate for errors in CNC machine. Experimentally validated with impact hammer tests, achieving less than 1.5% error in the first three natural frequencies. Real-time vibration signals were acquired under different clamping conditions and analyzed using RMS (root mean square) and PCA (principal component analysis) techniques for identifying abnormal patterns. PDPS was established to be more than 90% accurate in detecting eccentric clamping and vibration anomalies, enabling proactive maintenance and reducing machine downtime. The study facilitates the development of intelligent machine tool systems and supports Industry 4.0 targets through the adoption of virtual modeling, real-time monitoring, and intelligent control.
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Keywords: natural frequency, modal shape, vibration monitoring, impact hammer test, intelligent diagnosis system
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©
2026
CSME , ISSN 0257-9731
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