Application of singular spectrum analysis to tool wear detection using sound signals

Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture

Volumn 219, Issue 9, 2005, Pages 703-710

  Alonso, F J ^a   Salgado, D R ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

Flank wear; Singular spectrum analysis; Sound signal; Tool wear; Turning

Indexed keywords

ACOUSTIC WAVES; CONDITION MONITORING; FEATURE EXTRACTION; NEURAL NETWORKS; SIGNAL PROCESSING; TURNING;

FLANK WEAR; SINGULAR SPECTRUM ANALYSIS; SOUND SIGNAL; TOOL WEAR;

SPECTRUM ANALYSIS;

EID: 27644553923     PISSN: 09544054     EISSN: 09544054     Source Type: Journal    
DOI: 10.1243/095440505X32634     Document Type: Article

References (26)

1. Ravindra, H. V., Srinivasa, Y. G., and Krishnamurthy, R. Modelling of tool wear based on cutting forces in turning. Wear, 1993, 169, 25-32.
2. Dimla, E. and Dimla, Snr Sensor signals for tool-wear monitoring in metal cutting operations - a review of methods. Int. J. Mach. Tools Mf., 2000, 40, 1073-1098.
3. Weller, E. J., Schrier, H. M., and Weichbrodt, B. What sound can be expected from a worn tool? Trans. ASME, J. Engng for Industry, 1969, 91(3), 525-534.
4. Sadat, A. B. and Raman, S. Detection of tool flank wear using acoustic signature analysis. Wear, 1987, 115(3), 265-272.
5. Trabelsi, H. and Kannatey-Asibu, Jr, E. Pattern-recognition analysis of sound radiation in metal cutting. Int. J. Adv. Mfg Technol., 1991, 6, 220-231.
6. Delio, T., Tlusty, J., and Smith, S. Use of audio signals for chatter detection and control. Trans. ASME, J. Engng for Industry, 1992, 114, 146-157.
7. Silva, R. G., Reuben, R. L., Baker, K. J., and Wilcox, S. J. Tool wear monitoring of turning operations by neural network classification of a feature set generated from multiple sensors. Mech. Syst. Signal Process., 1998, 12, 319-332.
8. Lu, M. G. and Kaimatey-Asibu, Jr, E. Analysis of sound characteristics associated with adhesive wear in machining. Trans. NAMRI, 2000, 28, 257-262.
9. Silva, R. G., Baker, K. J., and Wilcox, S. J. The adaptability of a tool wear monitoring system under changing cutting conditions. Mech. Syst. Signal Process., 2000, 14(2), 287-298.
10. Kopač, I. and Sali, S. Tool wear monitoring during the turning process. J. Mater. Process. Technol., 2001, 113(1-3), 312-316.
11. Lu, M. G. and Kaimatey-Asibu, Jr, E. Analysis of sound signal generation due to flank wear in turning. Trans. ASME J. Mfg Sci. Engng, 2002, 124(4), 799-808.
12. Lu, M. G. and Kannatey-Asibu, Jr, E. Flank wear and process characteristic effect on system dynamics in turning, Trans. ASME f. Mfg Sci. Engng, 2004, 126(1), 131-140.
13. Golyandina, N., Nekrutkin, V., and Zhigljavsky, A. Analysis of time series structure - SSA and related techniques, 2001, pp. 13-78 (Chapman & Hall/CRC Press, Boca Raton, Florida).
14. Vautard, R. and Ghil, M. Singular spectrum analysis in non-linear dynamics, with applications to paleoclimatic time series. Physica D, 1989, 35, 395-424.
15. Vautard, R., Yiou, P., and Ghil, M. Singular spectrum analysis: a toolkit for short noisy chaotic signals. Physica D, 1992, 58, 95-126.
16. Pasmanter, R. A. Variational search of periodic motions in complex dynamical systems. Chaos Solitons Fractals, 1995, 6, 447-454.
17. Wang, W. J., Chen, J., and Wu, X. K. The application of some non-linear methods in rotating machinery fault diagnosis. Mech. Syst. Signal Process., 2001, 15, 697-705.
18. Choudhury, S. K., Jain, V. K., and Rama Rao, Ch. V. V. On-line monitoring of tool wear in turning using a neural network. Int. J. Mach. Tools Mf., 1999, 39, 489-504.
19. Yusuf Altintas, Q. On-line monitoring of flank wear in turning with multilayered feed-forward neural network. Int. J. Mach. Tools Mf., 1999, 39, 1945-1959.
20. Dutta, R. K., Paul, S., and Ghattopadhyay, A. B. Applicability of the back-propagation algorithm in tool condition monitoring for faster convergence. J. Mater. Process. Technol., 2000, 98, 299-309.
21. Zawada-Tomkiewicz, A. Classifying the wear of turning tools with neural networks. J. Mater. Process. Technol., 2001, 109, 300-304.
22. Niranjan Prasad, K. and Ramamoorthy, B. Tool wear evaluation by stereo vision and prediction by artificial neural networks. J. Mater. Process. Technol., 2001, 112, 43-52.
23. Ozel, T. and Nadgir, A. Prediction of flank wear by using backpropagation neural network modelling when cutting hardened H-13 steel with chamfered and honed CBN tools. Int. J. Mach. Tools Mf., 2002, 42, 287-297.
24. Scheffer, C., Kratz, H., Heyns, P. S., and Klocke, F. Development of a tool wear-monitoring system for hard turning. Int. J. Mach. Tools Mf., 2003, 43(10), 973-985.
25. Rahman, M., Zhou, Q., and Hong, G. S. On-line cutting state recognition in turning using a neural network. Int. J. Adv. Mfg Technol., 1995, 10, 87-92.
26. Qin, S. J. and Rajagopal, B. Combining statistics and expert systems with neural networks for empirical process modelling, In Proceedings of International Conference and Exhibition on Instrumentation Control, 1993, Vol. 48, pp. 1711-1720.