On the Applicability of Machine Learning Methods for Vibration-Based Predictive Tool Maintenance: A Case Study
DOI:
https://doi.org/10.33038/jcegi.7314Keywords:
vibration diagnostics, predictive maintenance, machine learning, sustainable manufacturingAbstract
This study presents a machine learning–based approach for predicting tool wear and preventing tool breakage using vibration diagnostics in machining processes. By analysing vibration signals (and, where applicable, acoustic emission), the proposed method enables early fault detection and supports predictive maintenance strategies. The approach contributes to sustainable manufacturing by reducing material waste, improving resource efficiency, and extending tool lifetime. Experimental results demonstrate that vibration features effectively distinguish between normal and abnormal tool conditions, highlighting the potential of AI-assisted diagnostics in green innovation and Industry 4.0 applications.
References
ABELLÁN-NEBOT, J. V. – SUBIRÓN, F. R. (2010): A review of machining monitoring systems based on artificial intelligence process models. International Journal of Advanced Manufacturing Technology, 47:237–257. https://doi.org/10.1007/s00170-009-2191-8
AUDACITY TEAM (2024): Audacity: Free, open source, cross-platform audio software. https://www.audacityteam.org/. Accessed: 2025-10-07.
BOASHASH, B. (2003): Introduction to the Concepts of TFSAP, in Time-Frequency Signal Analysis and Processing: A Comprehensive Reference. Brisbane: Queensland University of Technology, Brisbane.
BOEHMKE, B. – GREENWELL, B (2019): Hands-on Machine Learning with R. Chapman and Hall/CRC, New York.
CAUSOL, A. J. – ZEGARRA, F. C. – VARGAS-MACHUCA, J. – CORONADO, A. M. (2021): Optimal window size for the extraction of features for tool wear estimation., IEEE XXVIII International Conference on Electronics, Electrical Engineering and Computing (INTERCON). https://doi.org/10.1109/INTERCON52678.2021.9532759
DORNFELD, D. A. – DIEI, E. N. (1987): A model of tool fracture generated acoustic emission during machining. Journal of Engineering for Industry, 109:227–233. https://doi.org/10.1115/1.3187123
KISHAWY, H. A. – HEGAB, H. – UMER, U. – MOHANY, A. (2018): Application of acoustic emissions in machining processes: Analysis and critical review. International Journal of Advanced Manufacturing Technology, 98:1391–1407. https://doi.org/10.1007/s00170-018-2341-y
KRISHNAKUMAR, P. – RAMESHKUMAR, K. – RAMACHANDRAN, K. I. (2018): Acoustic emission-based tool condition classification in a precision high-speed machining of titanium alloy: A machine learning approach. International Journal of Computational Intelligence and Applications, 17. https://doi.org/10.1142/S1469026818500177
KUHN, M. (2008): Building predictive models in R using the caret package. Journal of Statistical Software, 28(5):1–26. https://doi.org/10.18637/jss.v028.i05
MATHWORKS (2025): Matlab. https://www.mathworks.com. Accessed: 2025-10-07.
MOHAMED, A. – HASSAN, M. – M’SAOUBI, R. – ATTIA, H. (2022): Tool condition monitoring for high-performance machining systems—a review. Sensors, 22:1–31. https://doi.org/10.3390/s22062206
POSIT (2025): Rstudio. https://posit.co/download/rstudio-desktop/.
RANDALL, R. B. (2013): A history of cepstrum analysis and its application to mechanical problems. In Surveill. 7 International Conference. https://doi.org/10.1016/j.ymssp.2016.12.026
RANDALL, R. B. – TECH, B. (1973): Cepstrum Analysis and Gearbox Fault Diagnosis. Naerum.
ZSIDAI, L. – MARKOVICS, B. – PATAKI, T. – SZAKÁL, Z. – SZABÓ, I. – SCHREMPF, N. – KERESZTES, R. (2025): Development of laboratory static fracture tests for milling tools. Mechanical Engineering Letters, 26(1):121–139.
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