Comparison of artificial intelligence techniques for cutting tool condition monitoring

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Comparison of artificial intelligence techniques for cutting tool condition monitoring

COLANTONIO Lorenzo, EQUETER Lucas, DEHOMBREUX Pierre, DUCOBU François

Abstract. In manufacturing, tool wear monitoring is crucial as it directly influences production quality and operating costs. Inaccurate replacement strategies can result in increased costs and substandard parts. Various Artificial Intelligence (AI) methods have been proposed to monitor tool wear using cutting signals, but a comprehensive performance comparison is lacking. This paper evaluates three distinct AI approaches: Artificial Neural Networks (ANN), Support Vector Machines (SVM), and K-Nearest Neighbours (K-NN). The selection of these approach is based on their learning mechanisms. Each method is optimized using a GridSearch algorithm and their real-time wear monitoring capabilities are compared. The results shows that all AI techniques monitored tool wear with similar precision, making it challenging to draw a definitive conclusion in this regard. The choice of the most appropriate AI method is heavily dependent on the manufacturing environment. For large-scale manufacturing under similar cutting conditions, K-NN and SVM are a good choice. The ANN is better suited to all scenarios, but particularly where there are substantial fluctuations in cutting conditions or, in general, larger databases.

Keywords
Machining, Artificial Intelligence, Wear, Monitoring

Published online 4/24/2024, 10 pages
Copyright © 2024 by the author(s)
Published under license by Materials Research Forum LLC., Millersville PA, USA

Citation: COLANTONIO Lorenzo, EQUETER Lucas, DEHOMBREUX Pierre, DUCOBU François, Comparison of artificial intelligence techniques for cutting tool condition monitoring, Materials Research Proceedings, Vol. 41, pp 1962-1971, 2024

DOI: https://doi.org/10.21741/9781644903131-217

The article was published as article 217 of the book Material Forming

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.

References
[1] W. Xu and L. Cao, ‘Optimal tool replacement with product quality deterioration and random tool failure’, Int. J. Prod. Res., vol. 53, no. 6, pp. 1736–1745, Mar. 2015. https://doi.org/10.1080/00207543.2014.957878.
[2] M. Kuntoğlu et al., ‘A Review of Indirect Tool Condition Monitoring Systems and Decision-Making Methods in Turning: Critical Analysis and Trends’, Sensors, vol. 21, no. 1, p. 108, 2020. https://doi.org/10.3390/s21010108.
[3] F. Halila, C. Czarnota, and M. Nouari, ‘Analytical stochastic modeling and experimental investigation on abrasive wear when turning difficult to cut materials’, Wear, vol. 302, no. 1–2, pp. 1145–1157, 2013. https://doi.org/10.1016/j.wear.2012.12.055.
[4] P. Muthuswamy and S. K, ‘Artificial intelligence based tool condition monitoring for digital twins and industry 4.0 applications’, Int. J. Interact. Des. Manuf. IJIDeM, vol. 17, no. 3, pp. 1067–1087, Jun. 2023. https://doi.org/10.1007/s12008-022-01050-5.
[5] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, ‘A Systematic Literature Review of Cutting Tool Wear Monitoring in Turning by Using Artificial Intelligence Techniques’, Machines, vol. 9, no. 12, p. 351, Dec. 2021. https://doi.org/10.3390/machines9120351.
[6] R. U. Baig, S. Javed, M. Khaisar, M. Shakoor, and P. Raja, ‘Development of an ANN model for prediction of tool wear in turning EN9 and EN24 steel alloy’, Adv. Mech. Eng., vol. 13, no. 6, p. 168781402110267, Jun. 2021. https://doi.org/10.1177/16878140211026720.
[7] D. Y. Pimenov, A. Bustillo, S. Wojciechowski, V. S. Sharma, M. K. Gupta, and M. Kuntoğlu, ‘Artificial intelligence systems for tool condition monitoring in machining: analysis and critical review’, J. Intell. Manuf., vol. 34, no. 5, pp. 2079–2121, Jun. 2023. https://doi.org/10.1007/s10845-022-01923-2.
[8] L. Colantonio, L. Equeter, P. Dehombreux, and F. Ducobu, ‘Comparison of cutting tool wear classification performance with artificial intelligence techniques’, pp. 1265–1274, May 25, 2023.
[9] A. J. Bishara and J. B. Hittner, ‘Testing the significance of a correlation with nonnormal data: Comparison of Pearson, Spearman, transformation, and resampling approaches.’, Psychol. Methods, vol. 17, no. 3, pp. 399–417, Sep. 2012. https://doi.org/10.1037/a0028087.
[10] T. Rabczuk and K.-J. Bathe, Eds., Machine Learning in Modeling and Simulation: Methods and Applications. in Computational Methods in Engineering & the Sciences. Cham: Springer International Publishing, 2023. doi: 10.1007/978-3-031-36644-4.
[11] H. Li, Machine Learning Methods. Singapore: Springer Nature Singapore, 2024. doi: 10.1007/978-981-99-3917-6.