Prediction of drill failure using features extraction in time and frequency domains of feed motor current

International Journal of Machine Tools and Manufacture

Volumn 48, Issue 1, 2008, Pages 29-39

  Choi, Young Jun ^a   Park, Min Soo ^b   Chu, Chong Nam ^b  

^a LG Electronics Inc   (South Korea)

^b Seoul National University   (South Korea)

Author keywords

Characteristic parameters of drill failure; Dill state index; Feed motor current; Neural network; Wavelet transform

Indexed keywords

ALGORITHMS; CUTTING; FAILURE ANALYSIS; NEURAL NETWORKS; PARAMETER ESTIMATION; WAVELET TRANSFORMS;

CHARACTERISTIC PARAMETERS OF DRILL FAILURES; DILL STATE INDEX; FEED MOTOR CURRENTS; SPINDLE ROTATIONAL FREQUENCY;

DRILLING MACHINES (MACHINE TOOLS);

EID: 35348863189     PISSN: 08906955     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.ijmachtools.2007.08.009     Document Type: Article

References (28)

1. Y.T. Oh, Cutting torque control in drilling using spindle motor current, Ph.D. Thesis, School of Mechanical and Aerospace Engineering, Seoul National University, Korea, 2001.
2. Thangaraj A., and Wright P.K. Computer-assisted prediction of drill-failure using in-process measurements of thrust force. Transactions of the ASME, Journal of Engineering for Industry 110 (1988) 192-200
3. Hong S.Y., Ni J., and Wu S.M. Analysis of drill failure modes by multi-sensors on a robotic end effector. Transactions of the ASME, Journal of Manufacturing Science and Engineering 118 4 (1996) 672-676
4. Subramanian K., and Cook N.H. Sensing of drill wear and prediction of drill life. Transactions of the ASME, Journal of Engineering for Industry 99 (1977) 295-301
5. Liua H.S., Leea B.Y., and Tarng Y.S. In-process prediction of corner wear in drilling operations. Journal of Materials Processing Technology 101 (2000) 152-158
6. Kaldor S., and Lenz E. Investigation in tool life of the twist drills. Annals of the CIRP 29 1 (1980) 23-27
7. Quadro A.L., and Branco J.R.T. Analysis of the acoustic emission during drilling test. Surface and Coating Technology 94-95 1-3 (1997) 691-695
8. Ravindra H.V., Srinivasa Y.G., and Krishnamurthy R. Acoustic emission for tool condition monitoring in metal cutting. Wear 212 (1997) 78-84
9. Abu-Mahfouz I. Drilling wear detection and classification using vibration signals and artificial neural network. International Journal of Machine Tools and Manufacture 43 (2003) 707-720
10. Oh Y.T., Kwon W.T., and Chu C.N. Drilling torque control using spindle motor current and its effect on tool wear. International Journal of Advanced Manufacturing Technology 24 (2004) 327-334
11. Li X. On-line detection of the breakage of small diameter drills using current signature wavelet transform. International Journal of Machine Tools and Manufacture 39 1 (1999) 157-164
12. Kim H.Y., Ahn J.H., Kim S.H., and Takata S. Real-time drill wear estimation based on spindle motor power. Journal of Materials Processing Technology 124 (2002) 267-273
13. Noori-Khajavi A., and Komanduri R. Frequency and time domain analyses of sensor signals in drilling, part 1: correlation with drill wear. International Journal of Machine Tools and Manufacture 35 6 (1995) 775-793
14. Jun M.B., Ozdoganlar O.B., Devor R.E., Kapoor S.G., Kirchheim A., and Schaffner G. Evaluation of a spindle-based force sensor for monitoring and fault diagnosis of machining operations. International Journal of Machine Tools and Manufacture 42 (2002) 741-751
15. Jantunen E. A summary of methods applied to tool condition monitoring in drilling. International Journal of Machine Tools and Manufacture 42 (2002) 997-1010
16. Li G.S., Lau W.S., and Zhang Y.Z. In-process drill wear and breakage monitoring for a machining center based on cutting force parameters. International Journal of Machine Tools and Manufacture 32 (1992) 855-867
17. D.W. Kim, Tool life improvement through peck drilling using thrust force monitoring in deep micro-hole drilling of steel, Ph. D. Thesis, School of Mechanical and Aerospace Engineering, Seoul National University, Korea, 2006.
18. Konig W., Kutzner K., and Schehl U. Tool monitoring of small drills with acoustic emission. International Journal of Machine Tools and Manufacture 32 4 (1992) 487-493
19. Li X. Real-time detection of the breakage of small diameter drills with wavelet transform. International Journal of Advanced Manufacturing Technology 14 (1998) 539-543
20. Lee J.M., Choi D.K., Kim J.W., and Chu C.N. Real time tool breakage monitoring for NC milling process. Annals of the CIRP 44 (1995) 59-62
21. Fua L., Linga S.F., and Tseng C.H. On-line breakage monitoring of small drills with input impedance of driving motor. Mechanical Systems and Signal Processing 21 1 (2007) 457-465
22. Y.J. Choi, Prediction of drill failure in CNC machining center using motor current, Ph. D. Thesis, School of Mechanical and Aerospace Engineering, Seoul National University, Korea, 2001.
23. Daubechies I. The wavelet transform, time-frequency localization and signal analysis. IEEE Transactions on Information Theory 36 (1990) 961-1005
24. Tansel I.N., Mekdeci C., Rodriguez O., and Uragun B. Monitoring drill conditions with wavelet based encoding and neural network. International Journal of Machine Tools and Manufacture 33 4 (1993) 559-575
25. Mallat S.G. A theory for multiresolution signal decomposition: the wavelet representation. IEEE Transactions on Pattern Analysis and Machine Intelligence 11 (1989) 674-693
26. Freeman J.A., and Skapura D.M. Neural Networks-Algorithms, Applications and Programming Techniques (1991), Addison-Wesley
27. Noori-Khajavi A., and Komanduri R. Frequency and time domain analyses of sensor signals in drilling, part 2: investigation of the problems associated with sensor integration. International Journal of Machine Tools and Manufacture 35 6 (1995) 795-815
28. Liu T.I., and Ko E.J. On-line recognition of drill wear via artificial neural networks, monitoring and control for manufacturing processes, PED. ASME 44 (1990) 101-110