Methods for on-line directionally independent failure prediction of end milling cutting tools

Machining Science and Technology

Volumn 11, Issue 1, 2007, Pages 1-43

  Suprock, Christopher A ^a   Roth, John T ^a  

^a BEHREND COLLEGE   (United States)

Author keywords

Autoregressive; Directional independence; Failure prediction; FFT; Force; Milling; On line; Pocketing

Indexed keywords

DIRECTIONAL INDEPENDENCE; FAILURE PREDICTION; FORCE; MILLING; POCKETING;

COMPUTATIONAL METHODS; CONDITION MONITORING; DYNAMOMETERS; FAILURE ANALYSIS; FAST FOURIER TRANSFORMS; MATHEMATICAL MODELS; MILLING (MACHINING); ONLINE SYSTEMS; SENSORS;

CUTTING TOOLS;

EID: 33847400980     PISSN: 10910344     EISSN: 15322483     Source Type: Journal    
DOI: 10.1080/10910340601174806     Document Type: Article

References (23)

1. Altintas, Y. (1992) Prediction of cutting forms and tool breakage in milling from feed drive current measurements. ASME Journal of Engineering for Industry, 110: 271-277.
2. Aoyama, H.I.; Inadaki, I.; Suda, I.; Ohzeki, H. (1988) Prediction of tool wear and tool failure in milling by utilizing magnetostrictive torque sensor. Technical Papers of the North American Manufacturing Research Institute SME, 26: 12.5-130.
3. Box, G.E.P.; Jenkins, G. (1976) Time Series Analysis: Forecasting and Control, Holden-Day, San Francisco, CA.
4. Bukkapatnam, S.T.; Kumara, S.R.; Lakhtakia, A.; Srinivasan. (2002) The neighborhood method and its coupling with the wavelet method for signal separation of chaotic signals. Signal Process, 82(10): 1351-1374.
5. Chen, J.C.; Ghen, J.G. (2005) An artificial-neural-networks-based in-process tool wear prediction system in milling operations. The International, Journal of Advanced Manufacturing Technology, 25(5-6): 427-434.
6. Jemielniak, K. (1999) Commercial tool condition monitoring systems. The International, Journal of Advanced Manufacturing Technology, 15(10): 711-721.
7. Kamarthi, S.V.; Kumara, S.R.T.; Cohen, P.H. (2000) Flank wear estimation in turning through wavelet representation of acoustic emission signals. Journal of Manufacturing Science and Engineering, 122(1): 12-19.
8. Kubo, T.; Murao, M. (2000) A machine diagnostic study for in-process quality control using neural network. ASME Proceedings of the 2000 Japan - USA Flexible Automation Conference, 2000JUSFA-13099.
9. Liang, S.Y.; Hecker, R.L.; Landers, R.G. (2004) Machining process monitoring and control: The state-of-the-art. Journal of Manufacturing Science and Engineering, 126(2): 297-310.
10. Nagasaka, K.; Yamakawa, A.; Honda, K.; Ichihashi, H.; Nakagawa, H.; Hirogaki, T.; Kita, Y.; Kakino, Y. (2001) Discrimination of the tool failure patterns with simultaneous approach of fuzzy clustering, principle components and multiple regression analysis. Proc. of 16th International Conference on Production Research, #0510.
11. Pandit, S.M. (1991) Modal and Spectrum Analysis: Data Dependent Systems in State Space, John Wiley & Sons, Inc., New York.
12. Pandit, S.M.; Wu, S. (1983) Time Series and System Analysis with Applications, John Wiley & Sons, Inc., New York.
13. Rehorn, A.G.; Jiang, J.; Orban, P.E. (2005) State-of-the-art methods and results in tool condition monitoring: a review. The International Journal of Advanced Manufacturing Technology, 26(7-8): 693-710.
14. Rene, J.R.; Gilbertob, H.; Ivanc, T.; Juan, G.J. (2004) FPGA based on-line tool breakage detection system for CNC milling machines. Mechatronics, 14(4): 439-454.
15. Roth, J.T.; Pandit, S.M. (1999a) Using multivariate models to monitor end mill wear and predict tool failure. Technical Papers of the North American Manufacturing Research Institute of SME, 27: 63-68.
16. Roth, J.T.; Pandit, S.M. (1999b) Monitoring end mill wear and predicting tool failure using accelerometers. Transactions of the ASME Journal of Manufacturing Science and Engineering, 121(4): 559-567.
17. Roth, J.T. (2006) Using the eigenvalues of multivariate spectral matrices to achieve cutting direction and sensor orientation independence. Transactions of the ASME Journal of Manufacturing Science and Engineering, 128(1): 350-354.
18. Sick, B. (2002) Online 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, 16: 487-546.
19. Susanto, V.; Chen, J.C. (2002) Fuzzy logic based in-process tool-wear monitoring system in face milling operations. The International Journal of Advanced Manufacturing Technology, 21 (3): 186-192.
20. Tansel, I.N.; Mekdeci C.; McLaughlin C. (1995) Detection of tool failure in end milling with wavelet transformations and neural networks (WT-NN). International Journal, of Machine Tools and Manufacture, 35(8): 1137-1147.
21. Tönshoff, H.K.; Li X.; Lapp, C. (2003) Application of fast Haar transform and concurrent learning to tool-breakage detection in milling. Mechatronics, IEEE/ASME Transactions, 8(3): 414-417.
22. Varma, S.; Baras, J.S.; Shamma, S.A. (2001) Biologically inspired acoustic wear analysis. Center for Auditory and Acoustics Research, University of Maryland at College, Park, CAAR TR 2001-1 (ISR TR 2001-11).
23. Wang, L.; Mehrabi, M.G.; Kannatey-Asibu, E. (2001) Tool wear monitoring in machining processes through wavelet analysis. Transactions of the North American Manufacturing Research Institute of SME, 29: 399-406.