Monitoring and Prediction of Tool Wear in Turning Process using Machine Learning Technique
S.Y. QUEK, W.K. LEE, N. TALIB, H. ABDULLAH, A.M SABRI, A. SUNG
Abstract. The benefit of an intelligent monitoring system is which can detect the changes that could indicate a developing fault of cutting tool more earlier to avoid the catastrophic failure of turning machine. The worn cutting tool can be replaced in time to ensure the accuracy of the cutting process. The objective of this study is to apply support vector machine prediction model to predict the flank wear during turning process. Experiments have been conducted to measure tool wear based on the factorial design technique in a turning of AISI 1045 carbon steel using carbide insert. Then, the cuttings conditions and statistical features from vibration signal in time domain were used as the input of regression model corresponding to the output, flank wear for the tool wear prediction by using MATLAB. The results showed that polynomial kernel prediction model performed outstanding which had the highest accuracy with 93.57% among the three models. The R2 value for gaussian and polynomial kernel were stated 0.9956 and 0.9866 respectively. While the linear kernel had the lowest of R2 value which was only 0.6297.
Keywords
Tool Wear, Machine Learning Technique, Performance Analysis
Published online 7/15/2026, 6 pages
Copyright © 2026 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA
Citation: S.Y. QUEK, W.K. LEE, N. TALIB, H. ABDULLAH, A.M SABRI, A. SUNG, Monitoring and Prediction of Tool Wear in Turning Process using Machine Learning Technique, Materials Research Proceedings, Vol. 68, pp 322-327, 2026
DOI: https://doi.org/10.21741/9781644904237-43
The article was published as article 43 of the book Green Materials, Processing & Characterization
Content from this work may be used under the terms of the Creative Commons Attribution 3.0 license. Any further distribution of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.
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