Application of machine learning algorithms for recognizing the wear of the cutting tool during precision milling of hardened tool steel

• Autorzy: Twardowski Paweł , Tabaszewski Maciej , Tabaszewski Mateusz , Czyżycki Jakub

Identyfikatory

DOI

10.12913/22998624/196706

• Języki publikacji: EN

Abstrakty

EN

The paper presents extensive research on tool wear and the analysis of diagnostic measures for different cutting speeds (vc). The work is divided into two parts. The first part involves conducting an experiment on a machining center, measuring the tool wear index, and recording vibration acceleration signals, followed by analyzing the obtained results. In the second part, the authors focus on determining appropriate diagnostic signal measures and their selection and applying various machine learning methods. The machine learning pertains to classifying the tool condition as operational or non-operational. The best of the tested classifiers achieved an accuracy of 0.999. Thanks to the comparative analysis, it was possible to propose a condition monitoring method that is based only on vibration acceleration without taking into account the cutting speed parameter. Vibration measurement can be performed on the spindle. In this case, the weighted accuracy value determined on the test set was 0.993. The F1 coefficient characterizing both precision and accuracy was 0.982. The authors consider this result to be satisfactory in industrial conditions. Measurement on the spindle without the need to take into account the cutting speed is easy to use in industrial practice

Słowa kluczowe

EN

milling; hardened steel; tool wear; diagnostics; machine learning

• Wydawca: Lublin University of Technology ; Polish Society of Ecological Engineering (PTIE), Branch of PTIE in Lublin
• Czasopismo: Advances in Science and Technology. Research Journal
• Rocznik: 2025
• Tom: Vol. 19, no. 2
• Strony: 365--382
• Opis fizyczny: Bibliogr. 30 poz., fig., tab.

Twórcy

autor

Twardowski Paweł

• Institute of Mechanical Technology Poznan University of Technology, The Faculty of Mechanical Engineering, Pl. Marii Skłodowskiej-Curie 5, 60-965 Poznań, Poland

• https://orcid.org/0000-0002-1823-6279

autor

Tabaszewski Maciej

• Institute of Technical Mechanics The Faculty of Mechanical Engineering, Poznan University of Technology, Pl. Marii Skłodowskiej-Curie 5, 60-965 Poznań, Poland

autor

Tabaszewski Mateusz

• Student at the Faculty of Computing and Telecommunications, Poznan University of Technology, Pl. Marii Skłodowskiej-Curie 5, 60-965 Poznań, Poland

autor

Czyżycki Jakub

• Institute of Mechanical Technology Poznan University of Technology, The Faculty of Mechanical Engineering, Pl. Marii Skłodowskiej-Curie 5, 60-965 Poznań, Poland

Bibliografia

• 1. Zhou Y, Xue W. Review of tool condition monitoring methods in milling processes. Int J Adv Manuf Technol. 2018 May; 96(5–8): 2509–23. https://doi.org/10.1007/s00170-018-1768-5.
• 2. Pimenov DYu, Kumar Gupta M, Da Silva LRR, Kiran M, Khanna N, Krolczyk GM. Application of measurement systems in tool condition monitoring of Milling: A review of measurement science approach. Measurement. 2022 Aug; 199: 111503. https://doi.org/10.1016/j.measurement.2022.111503.
• 3. Patra K, Jha AK, Szalay T, Ranjan J, Monostori L. Artificial neural network based tool condition monitoring in micro mechanical peck drilling using thrust force signals. Precision Engineering. 2017 Apr; 48: 279–91. https://doi.org/10.1016/j.precisioneng.2016.12.011.
• 4. Huang PB, Ma CC, Kuo CH. A PNN self-learning tool breakage detection system in end milling operations. Applied Soft Computing. 2015 Dec; 37: 114–24. https://doi.org/10.1016/j.asoc.2015.08.019.
• 5. Ratava J, Lohtander M, Varis J. Tool condition monitoring in interrupted cutting with acceleration sensors. Robotics and Computer-Integrated Manufacturing. 2017 Oct; 47: 70–5. https://doi.org/10.1016/j.rcim.2016.11.008.
• 6. Navarro-Devia JH, Chen Y, Dao DV, Li H. Chatter detection in milling processes—a review on signal processing and condition classification. Int J Adv Manuf Technol. 2023 Apr; 125(9–10): 3943–80. https://doi.org/10.1007/s00170-023-10969-2.
• 7. Lamraoui M, Thomas M, El Badaoui M. Cyclostationarity approach for monitoring chatter and tool wear in high-speed milling. Mechanical Systems and Signal Processing. 2014; 44(177–198). https://doi.org/10.1016/j.ymssp.2013.05.001.
• 8. Twardowski P, Tabaszewski M, Wiciak – Pikuła M, Felusiak-Czyryca A. Identification of tool wear using acoustic emission signal and machine learning methods. Precision Engineering. 2021 Nov; 72: 738–44. https://doi.org/10.1016/j.precisioneng.2021.07.019.
• 9. Filippov AV, Rubtsov VE, Tarasov SYu. Acoustic emission study of surface deterioration in tribocontacting. Applied Acoustics. 2017 Feb; 117: 106–12. https://doi.org/10.1016/j.apacoust.2016.11.007.
• 10. Kishawy HA, Hegab H, Umer U, Mohany A. Application of acoustic emissions in machining processes: analysis and critical review. Int J Adv Manuf Technol. 2018 Sep; 98(5–8): 1391–407. https://doi.org/10.1007/s00170-018-2341-y.
• 11. Jessel T, Byrne C, Eaton M, Merrifield B, Harris S, Pullin R. Tool condition monitoring of diamond-coated burrs with acoustic emission utilizing machine learning methods. Int J Adv Manuf Technol. 2024 Jan; 130(3–4): 1107–24. https://doi.org10.1007/s00170-023-12700-7.
• 12. Ren Q, Balazinski M, Baron L, Jemielniak K, Botez R, Achiche S. Type-2 fuzzy tool condition monitoring system based on acoustic emission in micromilling. Information Sciences. 2014 Jan; 255: 121–34. https://doi.org/10.1016/j.ins.2013.06.010.
• 13. Pimenov DY, Bustillo A, Wojciechowski S, Sharma VS, Gupta MK, Kuntoğlu M. Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review. J Intell Manuf. 2023 Jun; 34(5): 2079–121. https://doi.org/10.1007/s10845-022-01923-2.
• 14. Serin G, Sener B, Ozbayoglu AM, Unver HO. Review of tool condition monitoring in machining and opportunities for deep learning. Int J Adv Manuf Technol. 2020 Jul; 109(3–4): 953–74. https://doi.org/10.1007/s00170-020-05449-w.
• 15. Ngo T. Data mining: practical machine learning tools and technique, third edition by Ian H. Witten, Eibe Frank, Mark A. Hell. SIGSOFT Softw Eng Notes. 2011 Sep 30; 36(5): 51–2. https://doi.org/10.1145/2020976.2021004.
• 16. Sick B. On-line and indirect tool wear monitoring in turning with artificial neural networks: a review of more than a decade of research. Mechanical Systems and Signal Processing. 2002 Jul; 16(4): 487–546. https://doi.org/10.1006/mssp.2001.1460.
• 17. Kuntoğlu M, Aslan A, Sağlam H, Pimenov DY, Giasin K, Mikolajczyk T. Optimization and analysis of surface roughness, flank wear and 5 different sensorial data via tool condition monitoring system in turning of AISI 5140. Sensors. 2020 Aug 5; 20(16): 4377. https://doi.org/10.3390/s20164377.
• 18. Tabaszewski M, Twardowski P, Wiciak-Pikuła M, Znojkiewicz N, Felusiak-Czyryca A, Czyżycki J. Machine learning approaches for monitoring of tool wear during grey cast-iron turning. Materials. 2022 Jun 20; 15(12): 4359. https://doi.org/10.3390/ma15124359.
• 19. Mohanraj T, Shankar S, Rajasekar R, Sakthivel NR, Pramanik A. Tool condition monitoring techniques in milling process — a review. Journal of Materials Research and Technology. 2020 Jan; 9(1): 1032–42. https://doi.org/10.1016/j.jmrt.2019.10.031.
• 20. Twardowski P, Czyżycki J, Felusiak-Czyryca A, Tabaszewski M, Wiciak-Pikuła M. Monitoring and forecasting of tool wear based on measurements of vibration accelerations during cast iron milling. Journal of Manufacturing Processes. 2023 Jun; 95: 342–50. https://doi.org/10.1016/j.jmapro.2023.04.036.
• 21. Pandiyan V, Shevchik S, Wasmer K, Castagne S, Tjahjowidodo T. Modelling and monitoring of abrasive finishing processes using artificial intelligence techniques: A review. Journal of Manufacturing Processes. 2020 Sep; 57: 114–35. https://doi.org/10.1016/j.jmapro.2020.06.013.
• 22. Huang Z, Zhu J, Lei J, Li X, Tian F. Tool wear predicting based on multi-domain feature fusion by deep convolutional neural network in milling operations. J Intell Manuf. 2020 Apr; 31(4): 953–66. https://doi.org/10.1007/s10845-019-01488-7.
• 23. Geron A. Hands-On Machine Learning with Scikit-Learn and TensorFlow, O’Reilly Media, 2019.
• 24. Kornacki J., Ćwik J. Statystyczne systemy uczące się, WNT, Warszawa 2005 (in Polish).
• 25. Larose D. Discovering Knowledge in Data, An Introduction to Data Mining, John Wiley & Sons, Inc. 2005.
• 26. Tool steel WNLV/1.2714/55NiCrMoV7 chemical composition: https://akrostal.pl/en/stale/wnlv-1-2714-55nicrmov7/.
• 27. Cempel C. Vibroacoustic Condition Monitoring, Ellis Horwood, London, 1991.
• 28. Bonizzi P, Karel JMH, Meste O, Peeters RLM. Singular spectrum decomposition: a new method for time series decomposition. Adv Adapt Data Anal. 2014 Oct; 6(4): 1450011. https://doi.org/10.1142/S1793536914500113.
• 29. Alexandrov T. Method of Trend Extraction Using Singular Spectrum Analysis. REVSTAT-Statistical Journal. 2009 Apr 28; 1–22. https://doi.org/10.48550/arXiv.0804.3367.
• 30. Borodich Suarez S, Heravi S, Pepelyshev A. Forecasting industrial production indices with a new singular spectrum analysis forecasting algorithm. Statistics and Its Interface. 2023; 16(1): 31–42. https://doi.org/10.4310/SII.2023.v16.n1.a3.