Application of wavelet packet analysis in drill wear monitoring

Machining Science and Technology

Volumn 11, Issue 3, 2007, Pages 413-432

  Patra, Karali ^a   Pal, Surjya K ^a   Bhattacharyya, Kingshook ^a  

^a INDIAN INSTITUTE OF TECHNOLOGY   (India)

Author keywords

Artificial neural network; Current sensor; Drill wear; Tool condition monitoring; Wavelet packet transform

Indexed keywords

DRILLING; HALL EFFECT; NEURAL NETWORKS; SENSITIVITY ANALYSIS; TIME DOMAIN ANALYSIS; WAVELET TRANSFORMS;

CURRENT SENSORS; DRILL WEAR MONITORING; TOOL CONDITION MONITORING; WAVELET PACKET TRANSFORMS;

WAVELET ANALYSIS;

EID: 35148838606     PISSN: 10910344     EISSN: 15322483     Source Type: Journal    
DOI: 10.1080/10910340701539908     Document Type: Article

References (28)

1. Abu-Mahfouz, I. (2003) Drilling wear detection and classification using vibration signals and artificial neural network. International Journal of Machine Tools & Manufacture, 43: 707-720.
2. Abu-Mahfouz, I, (2005) Drill flank wear estimation using supervised vector quantization neural networks. Neural Computing and Application, 14(3): 167-175.
3. Byrne, G.; Dorfeld, D.; Inasaki, I.; Ketteler, G.; Konig, W.; Teti, R. (1995) Tool condition monitoring (TCM) - the status of research and industrial application. Annals of CIRP, 44(2): 541-567.
4. Daubechies, I. (1990) The wavelet transform, time-frequency localization and signal analysis. IEEE Transaction on Information Theory, 36(5): 961-1005.
5. Dimla Snr, D.E. (2000) Sensor signal for tool wear monitoring in metal cutting operations - a review of methods. International Journal of Machine Tools and Manufacture, 40: 1073-1098.
6. Dimla, D.E.; Lister, P.M.; Leighton, N.J. (1997) Neural network solution to the tool condition monitoring problem in metal cutting - a critical review of methods. International journal of Machine Tools and Manufacture, 37(9): 1219-1241.
7. Franco-Gasca, L.A.; Herrera-Ruiz, G.; Peniche-Vera, R.; Romero-Troncoso, R.D.J.; Leal-Tafolla, W. (2006) Sensorless tool failure monitoring system for drilling machines. International Journal of Machine Tools & Manufacture, 46: 381-386.
8. Haykin, S. (1998) Neural Network, A Comprehensive Foundation, 2nd edition, Pearson Education Pte. Ltd., Singapore, pp. 161-175.
9. Hong, G.S.; Rahman, M.; Zhou, Q. (1996) Using neural network for tool condition monitoring based on wavelet decomposition. International Journal of Machine Tools & Manufacture, 36(5): 551-566.
10. Huseyin, M.E.; Kenneth, A.L.; Hasan, O. (2001) Tool wear condition monitoring in drilling operations using hidden markov models (HMMs). International Journal of Machine Tools and Manufacture, 41: 1363-1384.
11. Jantunen, E. (2002) A summary of methods applied to tool condition monitoring in. drilling. International Journal of Machine Tools and Manufacture, 42: 997-1010.
12. Li, X.; Dong, S.; Yuan, Z. (1999) Discrete wavelet transform for tool breakage monitoring. International Journal of Machine Tools & Manufacture, 39: 1935-1944.
13. Li, X.; Tso, S.K.; Wang, J. (2000) Real-time tool condition monitoring using wavelet transforms and fuzzy techniques. IEEE Transactions on Systems, Man, and Cybernetics-part C: Applications and Reviews, 30(3): 352-357.
14. Liu, T. I.; Chen, W.Y.; Anatharaman, K. S. (1998) Intelligent detection of drill wear. Mechanical Systems and Signal Processing, 12(6): 863-873.
15. Mallat, S.G. (1989) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 11(5): 674-693.
16. Mannan, M.A.; Broms, S. (1989) Monitoring and adaptive control of cutting process by means of motor power and current measurements. Annals of CIRP, 38(1): 347-350.
17. Mathworks, Inc. (2004) Wavelet Toolbox, MATLAB 7.0.
18. Noori-Khajavi, A.; Komanduri, R. (1995) Frequency and time domain analysis of sensor signals in drilling -I. Correlation with drill wear. International Journal of Machine Tools and Manufacture, 35(6): 775-793.
19. Patra, K.; Pal, S.K.; Bhattacharyya, K. (2007) Artificial neural network based prediction of drill flank wear from motor current signals. Applied Soft Computing, 7: 929-935.
20. Sun, Q.; Tang, Y.; Ji, Y. (2005) Feature extraction with discrete wavelet transform for drill wear monitoring. Journal of Vibration and Control, 11(11): 1375-1396.
21. Tansel, I.N.; Mekdeci, C.; Rodriguez, O.; Uragan, B. (1993) Monitoring drill conditions with wavelet based encoding and neural networks. International Journal of Machine Tools and Manufacture, 33(4): 559-575.
22. Velayudham, A.; Krishnamurthy, R.; Soundarapandian, T. (2005) Acoustic emission based drill condition monitoring during drilling of glass/phenolic polymeric composite using wavelet packet transform. Materials Science and Engineering, A 412: 141-145.
23. Walnut, D.F. (2002) An Introduction To Wavelet Analysis, Birkhauser, Boston, pp. 335-368.
24. Xiaoli, L. (1999) On-line detection of the breakage of small diameter drills using current signature wavelet transform. International Journal of Machine Tools and Manufacture, 39: 157-164.
25. Xiaoli, L.; Xso, S.K. (1999) Drill wear monitoring based on current signals. Wear, 231: 172-178.
26. Xiaoli, L.; Zhejun, Y. (1998) Tool wear monitoring with wavelet packet transform-fuzzy clustering method. Wear, 219: 145-154.
27. Yao, Y.; Xiaoli, L.; Zhejun, Y. (1999) Tool wear detection with fuzzy classification and wavelet fuzzy neural network. International Journal of Machine Tools & Manufacture, 39: 1525-1538.
28. Zhang, M.Z.; Lui, Y.B.; Zhou, H. (2001) Wear mechanism maps of uncoated hss tools drilling die-cast aluminum alloy. Tribology International, 34: 727-731.