Developing automatic recognition system of drill wear in standard laminated chipboard drilling process

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The paper presents an automatic approach to recognition of the drill condition in a standard laminated chipboard drilling process. The state of the drill is classified into two classes: “useful” (sharp enough) and “useless” (worn out). The case “useless” indicates symptoms of excessive drill wear, unsatisfactory from the point of view of furniture processing quality. On the other hand the “useful” state identifies tools which are still able to drill holes acceptable due to the required processing quality. The main problem in this task is to choose an appropriate set of diagnostic features (variables), based on which the recognition of drill state (“useful” versus “useless”) can be made. The features have been generated based on 5 registered signals: feed force, cutting torque, noise, vibration and acoustic emission. Different statistical parameters describing these signals and also their Fourier and wavelet representations have been used for defining the features. Sequential feature selection is applied to detect the most class discriminative set of features. The final step of recognition is done by using three types of classifiers, including support vector machine, ensemble of decision trees and random forest. Six standard drills of 12 mm diameter with tungsten carbide tips were used in experiments. The results have confirmed good quality of the proposed diagnostic system.


  • [1] C. Scheffer, H. Kratz, P. S. Heyns and F. Klocke, “Development of a tool wear-monitoring system for hard turning”, International Journal of Machine Tools & Manufacture 43, 973–985 (2003).

  • [2] P.N. Botsaris and J.A. Tsanakas, “State-of-the-art in methods applied to tool condition monitoring (TCM) in unmanned machining operations: a review”, Proceedings of the International Conference of COMADEM, Prague, 73–87 (2008).

  • [3] D. E. Dimla and P. M. Lister, “On-line metal cutting tool condition monitoring. I: force and vibration analyses”, International Journal of Machine Tools & Manufacture. 40, 739–768 (2000).

  • [4] N. H. Abu-Zahra and G. Yu, “Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves”, International Journal of Machine Tools & Manufacture. 43(4), 33–343 (2003).

  • [5] K. Jemielniak, T. Urbański, J. Kossakowska J. and S. Bombiński, “Tool condition monitoring based on numerous signal features”, Int J. Adv. Manuf. Technol. 59, 73–81 (2012).

  • [6] R. Lemaster, L. Lu and S. Jackson, “The use of process monitoring techniques on a CNC wood router. Part 1. Sensor selection”, Forest Products Journal 50(7/8), 31–64 (2000).

  • [7] R. G. Silva, K. J. Baker and S. J. Wilcox, “The adaptability of a tool wear monitoring system under changing cutting conditions”, Mechanical Systems and Signal Processing 14, 287–298 (2000).

  • [8] J. Wilkowski and J. Górski, “Vibro-acoustic signals as a source of information about tool wear during laminated chipboard milling”, Wood Research 56(1), 57–66 (2011).

  • [9] R. J. Kuo, “Multi-sensor integration for on-line tool wear estimation through artificial neural networks and fuzzy neural network”, Engineering Applications of Artificial Intelligence 13, 249–261 (2000).

  • [10] C. Scheffer and P. S. Heyns, “Wear monitoring in turning operations using vibration and strain measurements”, Mechanical Systems and Signal Processing 15(6), 1185–1202 (2001).

  • [11] A. Noori-Khajavi and R. Komandur, “Frequency and time domain analyses of sensor signals in drilling-Part I”, International Journal of Machine Tools and Manufacture 35(6), 775–793 (1995).

  • [12] N. H. Abu-Zahra and G. Yu, “Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves”, International Journal of Machine Tools & Manufacture 43(4), 333–343 (2003).

  • [13] Q. Liu and Y Altintas, “On-line monitoring of flank wear in turning with multilayered feed-forward neural network”, International Journal of Machine Tools & Manufacture 39, 1945–1959 (1999).

  • [14] S. S. Panda, A. K. Singh, D. Chakraborty and S. K. Pal, “Drill wear monitoring using back propagation neural network”, Journal of Materials Processing Technology 172, 283–290 (2006).

  • [15] K. Patra, S. K. Pal and K. Bhattacharyya, “Artificial neural network based prediction of drill flank wear from motor current signals”, Applied Soft Computing 7, 929–935 (2007).

  • [16] P. Lezanski, “An intelligent system for grinding wheel condition monitoring”, Journal of Materials Processing Technology 109, 258–263 (2001).

  • [17] J. H. Zhou, C. K. Pang, Z. W. Zhong, and F. L. Lewis, “Tool wear monitoring using acoustic emissions by dominant-feature identification”, IEEE Transactions on Instrumentation and Measurement 60 (2), 547–559 (2011).

  • [18] M. V. Wickerhauser, Lectures on Wavelet Packet Algorithms, Washington University, 1991.

  • [19] I. Daubechies I., Ten Lectures on Wavelets, SIAM, Philadelphia, 1992.

  • [20] Matlab User Manual, Natick, MathWorks, 2014.

  • [21] V. Vapnik, Statistical Learning Theory, Wiley, New York, 1998.

  • [22] V. Kecman, Learning and Soft Computing: Support Vector Machines, Neural Networks, and Fuzzy Logic Models, MIT Press, Cambridge, 2001.

  • [23] T. Leś, S. Osowski, M. Kruk, “Automatic recognition of industrial tools using artificial intelligence approach”, Expert Systems with Application 40, 4777–4784 (2013).

  • [24] L. Breiman, “Random forests”, Machine Learning 45, 5–32 (2001).

Bulletin of the Polish Academy of Sciences Technical Sciences

The Journal of Polish Academy of Sciences

Journal Information

IMPACT FACTOR 2016: 1.156
5-year IMPACT FACTOR: 1.238

CiteScore 2016: 1.50

SCImago Journal Rank (SJR) 2016: 0.457
Source Normalized Impact per Paper (SNIP) 2016: 1.239


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