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Volume 13 | Issue 7 | Year 2026 | Article Id. IJME-V13I7P112 | DOI : https://doi.org/10.14445/23488360/IJME-V13I7P112

Time-Domain Vibration-Based Fault Diagnosis of Round Insert Face Milling Tools Using SVM and XGBoost: A Machine Learning Approach

Prashant Ashok Patil, Nitish Kumar Gautam, Bhuvaneshwar Patil

Received Revised Accepted Published
09 Mar 2026 30 Apr 2026 13 Jul 2026 30 Jul 2026

Citation :

Prashant Ashok Patil, Nitish Kumar Gautam, Bhuvaneshwar Patil, "Time-Domain Vibration-Based Fault Diagnosis of Round Insert Face Milling Tools Using SVM and XGBoost: A Machine Learning Approach," International Journal of Mechanical Engineering, vol. 13, no. 7, pp. 145-155, 2026. Crossref, https://doi.org/10.14445/23488360/IJME-V13I7P112

Abstract

Tool Condition Monitoring (TCM) plays a significant role in maintaining machining quality, reducing equipment idle time, and improving TCM often affords near-real-time detection of wear-out phenomena. A new method for vibration-based fault diagnosis of round insert face milling tools based on analysis in the time-domain and machine learning, the vibration signals were obtained using a spindle-integrated piezoelectric accelerometer during milling with controlled conditions. Healthy, flank wear, edge chipping, built-up edge and mixed fault conditions were studied from the statistical features extracted. Feature selection was implemented using Recursive Feature Elimination and Mutual Information ranking. Distributed computing was used to pilot SVM and XGBoost classifiers. The output indicated that the test accuracy of XGBoost was better (92.4% accuracy) than SVM (89.2%), while both had lower errors and shorter estimates as well. The presented approach is a cost-effective and real-time applicable method for intelligent monitoring of the condition of the monitoring tool in CNC machining. This study contributes to the development of data-driven predictive maintenance systems for smart manufacturing.

Keywords

Extreme Gradient Boosting (XGBoost), Fault Diagnosis, Machine Learning, Round Insert Tool, Support Vector Machine (SVM).

References

  1. Melvin Alexis Lara de Leon et al., “Tool Condition Monitoring Methods Applicable in the Metalworking Process,” Archives of Computational Methods in Engineering, vol. 31, no. 1, pp. 221-242, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  2. T. Benkedjouh et al., “Health Assessment and Life Prediction of Cutting Tools based on Support Vector Regression,” Journal of Intelligent Manufacturing, vol. 26, no. 2, pp. 213-223, 2013.
    [    CrossRef] [Google Scholar] [Publisher Link]
  3. Yuqing Zhou et al., “Time-Frequency Representation-Enhanced Transfer Learning for Tool Condition Monitoring During Milling of Inconel 718,” Maintenance and Reliability, vol. 25, no. 2, pp. 1-13, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  4. Thanassis Souflas et al., “Tool Condition Monitoring in Milling Using Cutting Force and Temperature Data from an Instrumented Milling Head,” The International Journal of Advanced Manufacturing Technology, vol. 139, no. 11, pp. 6049-6072, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  5. Muralidhar Ingale, Rupesh Bhortake, and Kishor Waghulde, “Vibration-Based Fault Diagnosis of Multi-Point Tool Inserts in Vertical Machining Centers Using Decision Tree, Random Forest Classifiers and Hybrid Stacking Model: A Machine Learning Approach,” Journal of Mines Metals and Fuels, vol. 73, no. 4, pp. 881-900, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  6. Mahardhika Pratama et al., “Online Tool Condition Monitoring Based on Parsimonious Ensemble+,” IEEE Transactions on Cybernetics, vol. 50, no. 2, pp. 664-677, 2018.
    [    CrossRef] [Google Scholar] [Publisher Link]
  7. Achmad Zaki Rahman et al., “Innovative Tool Condition Classification: Utilizing Time–Frequency Moments as Inputs for BiLSTM Networks in Milling Processes,” Journal of the Brazilian Society of Mechanical Sciences and Engineering, vol. 46, no. 9, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  8. Suhas S. Aralikatti, K. N. Ravikumar, and Hemantha Kumar, “Fault Diagnosis of Single-Point Cutting Tool Using Vibration Signal by Rotation Forest Algorithm,” SN Applied Sciences, vol. 1, no. 9, pp. 1-8, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  9. Swathi Kotha Amarnath et al., “Combining Sensor Fusion and a Machine Learning Framework for Accurate Tool Wear Prediction During Machining,” Machines, vol. 13, no. 2, pp. 1-15, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  10. T. Mohanraj et al., “Review of Advances in Tool Condition Monitoring Techniques in the Milling Process,” Measurement Science and Technology, vol. 35, no. 9, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  11. Agata Przybyś-Małaczek et al., “Application of Machine Learning Algorithms for Tool Condition Monitoring in Milling Chipboard Process,” Sensors, vol. 23, no. 13, pp. 1-31, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  12. D. Pradeep Kumar, V. Muralidharan, and Shaul Hameed Syed, “Multi-Point Tool Condition Monitoring System: A Comparative Study,” FME Transaction, vol. 50, no. 2, pp. 193-201, 2022.
    [    CrossRef] [Google Scholar] [Publisher Link]
  13. V Raja Sekhar Reddy et al., “Experimental Study of a Sensor Based Toolwear Detection Using Machine Learning Models,” International Journal of Scientific Research in Engineering and Management, vol. 10, no. 2, pp. 1-9, 2026.
    [    CrossRef] [Publisher Link]
  14. Muralidhar Ingale, Rupesh Bhortake, and Kishor Waghulde, “Vibration-Based Fault Diagnosis of Multi-Point Tool Inserts on a Vertical Machining Centre Using Histogram Features and Ensemble Voting Clasifiers,” 2025 5th Asian Conference on Innovation in Technology (ASIANCON), PIMPRI, India,  pp. 1-9, 2025. [CrossRef] [Google Scholar] [Publisher Link]
  15. Sameer Sayyad et al., “Milling Tool Wear State Classification and Prediction Using Machine Learning, Homogeneous and Heterogeneous Ensemble Learning Approach,” Journal of Remanufacturing, vol. 16, no. 1, pp. 1-26, 2026.
    [    CrossRef] [Google Scholar] [Publisher Link]
  16. Yang Hui et al., “Milling Tool Wear State Recognition by Vibration Signal Using a Stacked Generalization Ensemble Model,” Shock and Vibration, vol. 2019, no. 1, pp. 1-16, 2019.
    [    CrossRef] [Google Scholar] [Publisher Link]
  17. D. Vetrithangam et al., “Machine Fault Diagnosis Using Random Forest with Recursive Feature Elimination and Cross Validation,” Journal of Machine and Computing, vol. 5, no. 3, pp. 1700-1711, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  18. Agata Przybyś-Małaczek et al., “Comparative Study of Conventional Machine Learning versus Deep Learning-Based Approaches for Tool Condition Assessments in Milling Processes,” Applied Sciences, vol. 14, no. 13, pp. 5913-5913, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  19. Xintian Zi, Shangshang Gao, and Yang Xie, “An Online Monitoring Method of Milling Cutter Wear Condition Driven by Digital Twin,” Scientific Reports, vol. 14, no. 1, pp. 1-14, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  20. John Henry Navarro-Devia et al., “Chatter Detection in Milling Processes—A Review on Signal Processing and Condition Classification,” The International Journal of Advanced Manufacturing Technology, vol. 125, pp. 3943-3980, 2023.
    [    CrossRef] [Google Scholar] [Publisher Link]
  21. Matthew Alberts et al., “Chatter Detection in Simulated Machining Data: A Simple Refined Approach to Vibration Data,” The International Journal of Advanced Manufacturing Technology, vol. 132, pp. 4541-4557, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  22. Muhammet Ali Aykanat, and Rifat Kurban, “Comparative Study of Machine Learning and Ensemble Learning Approach on Tool Wear Classification,” Journal of Advances in Manufacturing Engineering, vol. 2, no. 6, pp. 86-93, 2025.
    [    CrossRef] [Google Scholar] [Publisher Link]
  23. Shixu Sun, Xiaofeng Hu, and Wenjuan Zhang, “Detection of Tool Breakage During Milling Process Through Acoustic Emission,” The International Journal of Advanced Manufacturing Technology, vol. 109, pp. 1409-1418, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]
  24. Ardalan F. Khalil, and Sarkawt Rostam, “Machine Learning-Based Predictive Maintenance for Fault Detection in Rotating Machinery: A Case Study,” Engineering, Technology & Applied Science Research, vol. 14, no. 2, pp. 13181-13189, 2024.
    [    CrossRef] [Google Scholar] [Publisher Link]
  25. Surin Khomfoi, and Leon M. Tolbert, “Fault Diagnostic System for Multilevel Inverter using Neural Network,” IEEE Transactions on Power Electronics, vol. 22, no. 3, pp. 1062-1069, 2007.
    [    CrossRef] [Google Scholar] [Publisher Link]
  26. Zhou Chang'an et al., “Vibration Singularity Analysis for Milling Tool Condition Monitoring,” International Journal of Mechanical Sciences, vol. 166, 2020.
    [    CrossRef] [Google Scholar] [Publisher Link]