Tool wear during turning operations can compromise surface integrity, dimensional precision, and production efficiency. This study introduces a machine learning-based framework for identifying tool wear conditions using the Support Vector Machine (SVM) algorithm. Vibration signals were acquired through impact excitation and analyzed in both time and frequency domains to extract statistical features representative of wear stages. To improve classification accuracy, Recursive Feature Elimination (RFE) was applied for optimal feature selection. The model was evaluated using a 5-fold cross-validation technique. Results indicate that the highest accuracy was achieved using frequency-domain features with RFE, yielding 100% accuracy in binary classification and 97.2% in multiclass classification. These outcomes emphasize the potential of the proposed approach as a reliable and non-invasive solution for tool wear monitoring in turning applications.
SooriMArezooB. Cutting tool wear prediction in machining operations, a review. J N Technol Mater2022; 12(2): 15–26.
2.
GuptaMKNiesłonyPSarikayaM, et al.Studies on geometrical features of tool wear and other important machining characteristics in sustainable turning of aluminium alloys. Int J Precis Eng Manuf-Green Technol2023; 10(4): 943–957. https://doi.org/10.1007/s40684-023-00501-y
3.
KasiviswanathanSGnanasekaranSThangamuthuM, et al.Machine-learning-and internet-of-things-driven techniques for monitoring tool wear in machining process: a comprehensive review. J Sens Actuator Netw2024; 13(5): 53. https://doi.org/10.3390/jsan13050053
4.
PatelDMuthuswamyS. Tool wear state recognition in CNC milling using reliable labels, robust domain features, and lightweight AI models: D. Patel, S. Muthuswamy. J Intell Manuf. 2025; 36: 1–36.
5.
MunaroRAttanasioADel PreteA. Tool wear monitoring with artificial intelligence methods: a review. J Manuf Mater Process2023; 7(4): 129. https://doi.org/10.3390/jmmp7040129
6.
MakhesanaMABaggaPJPatelKM, et al.Comparative analysis of different machine vision algorithms for tool wear measurement during machining. J Intell Manuf2024; 36: 1–25. https://doi.org/10.1007/s10845-024-02467-3
7.
PimenovDYda SilvaLRErcetinA, et al.State-of-the-art review of applications of image processing techniques for tool condition monitoring on conventional machining processes. Int J Adv Manuf Technol2024; 130(1): 57–85. https://doi.org/10.1007/s00170-023-12679-1
8.
HassanIUPanduruKWalshJ. An in-depth study of vibration sensors for condition monitoring. Sensors2024; 24(3): 740. https://doi.org/10.3390/s24030740
9.
PimenovDYBustilloAWojciechowskiS, et al.Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J Intell Manuf2023; 34(5): 2079–2121. https://doi.org/10.1007/s10845-022-01923-2
HanSYangQPattipatiKR, et al.Sensor selection and tool wear prediction with data‐driven models for precision machining. J Adv Manuf Processing2022; 4(4): e10143. https://doi.org/10.1002/amp2.10143
12.
ZhouYLiuCYuX, et al.Tool wear mechanism, monitoring and remaining useful life (RUL) technology based on big data: a review. SN Appl Sci2022; 4(8): 232. https://doi.org/10.1007/s42452-022-05114-9
13.
NCMMajumderHMulikS, et al.Data‐driven fault diagnosis of drilling tools: methods and applications. Eng Rep2025; 7(7): e70279. https://doi.org/10.1002/eng2.70279
14.
KovacevicF. Machine learning with vibrational measurement data of wind turbine environments. Hochschule für Angewandte Wissenschaften Hamburg, 2025.
15.
GomesMCBritoLCda SilvaMB, et al.Tool wear monitoring in micromilling using support vector machine with vibration and sound sensors. Precis Eng2021; 67: 137–151. https://doi.org/10.1016/j.precisioneng.2020.09.025
16.
Navarro DeviaJH. Experimental investigation of chatter occurrence and the effects of tool wear progress in milling titanium alloy, 2023.
17.
MishraDPattipatiKRBollasGM. Intelligent feature engineering for monitoring tool health in machining. In: Industry 4.0 Driven Manufacturing Technologies. Springer, 2024, pp. 37–67.
18.
WilsonAAnwarMR. The future of adaptive machine learning algorithms in high-dimensional data processing. International Transactions on Artificial Intelligence2024; 3(1): 97–107. https://doi.org/10.33050/italic.v3i1.656
19.
KamielBAkbarT. Sudarisman, and Krisdiyanto, cavitation detection of centrifugal pumps using SVM and statistical features. In: Recent Advances in Mechanical Engineering: Select Proceedings of ICOME 2021. Springer, 2022, pp. 1–9.
20.
WahulRMKambleVHSalunkeD. Diagnosing anomalous events leading to cutting tool faults through vibration signals trained on SMO-SVM. J Vib Eng Technol2025; 13(4): 247. https://doi.org/10.1007/s42417-025-01810-z
21.
ChengX. A comprehensive study of feature selection techniques in machine learning models, 2024. Available at SSRN 5154947.
22.
AllgaierJPryssR. Cross-validation visualized: a narrative guide to advanced methods. Mach Learn Knowl Extra2024; 6(2): 1378–1388. https://doi.org/10.3390/make6020065
23.
NtiIKNyarko-BoatengOAningJ. Performance of machine learning algorithms with different K values in K-fold cross-validation. Int J Inf Technol Comput Sci2021; 13(6): 61–71. https://doi.org/10.5815/ijitcs.2021.06.05
24.
GongWLiWShirakashiT, et al.An active method of monitoring tool wear states by impact diagnostic excitation. Int J Mach Tool Manufact2004; 44(7-8): 847–854. https://doi.org/10.1016/j.ijmachtools.2004.01.007
25.
PengSWangWChenY, et al.Regression-based hyperparameter learning for support vector machines. IEEE Transact Neural Networks Learn Syst2023; 99: 1–15.
26.
WangLLiXShiB, et al.Analysis and selection of eigenvalues of vibration signals in cutting tool milling. Adv Mech Eng2022; 14(1): 16878140221075197. https://doi.org/10.1177/16878140221075197
27.
IqbalMMadanA. CNC machine-bearing fault detection based on convolutional neural network using vibration and acoustic signal. J Vib Eng Technol2022; 10(5): 1613–1621. https://doi.org/10.1007/s42417-022-00468-1
