Tool wear detection in turning operations using singular spectrum analysis

Journal of Materials Processing Technology

Volumn 171, Issue 3, 2006, Pages 451-458

  Salgado, D R ^a   Alonso, F J ^a  

^a UNIVERSITY OF EXTREMADURA   (Spain)

Author keywords

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

Indexed keywords

NEURAL NETWORKS; PARAMETER ESTIMATION; PRINCIPAL COMPONENT ANALYSIS; SIGNAL PROCESSING; STATISTICAL METHODS; TIME SERIES ANALYSIS; TURNING;

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

WEAR OF MATERIALS;

EID: 30844471385     PISSN: 09240136     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.jmatprotec.2005.08.005     Document Type: Article

References (29)

1. G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. Konig, and R. Teti Tool condition monitoring - status of research and industrial application Ann. CIRP 44 2 1995 541 657
2. H.V. Ravindra, Y.G. Srinivasa, and R. Krishnamurthy Modelling of tool wear based on cutting forces in turning Wear 169 1993 25 32
3. E. Dimla Snr. Sensor signals for tool-wear monitoring in metal cutting operations - a review of methods Int. J. Mach. Tools Manuf. 40 2000 1073 1098
4. H. Ravindra, Y. Srinivasa, and R. Krishnamurthy Acoustic emission for tool condition monitoring in metal cutting Wear 212 1 1997 78 84
5. Y. Yao, and X.D. Fang Modelling of multivariate time series for tool wear estimation in finish-turning Int. J. Mach. Tools Manuf. 32 1992 495 508
6. D. Yan, T.I. Elwardany, and M.A. Elbestawi A multi sensor strategy for tool failure detection in end milling Int. J. Mach. Tools Manuf. 35 3 1995 383 398
7. W. Gong, T. Obikawa, and T. Shirakashi Monitoring of tool wear states in turning based on wavelet analysis JSME Int. J. Ser. III-Vibr. Contr. Eng. Ind. 40 1997 447 453
8. G.S. Hong, M. Rahman, and Q. Zhou Using neural network for tool condition monitoring based on wavelet decomposition Int. J. Mach. Tools Manuf. 36 5 1996 551 566
9. Y.M. Niu, Y.S. Wong, G.S. Hong, and T.I. Liu Multi-category classification of tool conditions using wavelet packets and ART2 network J. Manuf. Sci. Eng. -Trans. ASME 120 1998 807 816
10. N.H. Abu-Zahra, and G. Yu Gradual wear monitoring of turning inserts using wavelet analysis of ultrasound waves Int. J. Mach. Tools Manuf. 43 2003 337 343
11. V.B. Jammu, K. Danai, and S. Malkin Unsupervised neural network for tool breakage detection in turning Ann. CIRP 42 1 1993 67 74
12. R.G. Silva, R.L. Reuben, K.J. Baker, and S.J. Wilcox Tool wear monitoring of turning operations by neural network and expert system classification of a feature set generated from multiple sensors Mech. Syst. Signal Proc. 12 2 1998 319 332
13. S. Das, R. Islam, and A.B. Chattopadhyay A simple approach for on line tool wear monitoring using the analytic hierarchy process Proc. Inst. Mech. Eng. B: J. Eng. Manuf. 221 1997 19 27
14. S. Das, and A.B. Chattopadhyay Application of the analytic hierarchy process for estimating the state of the tool wear Int. J. Mach. Tools Manuf. 43 2003 1 6
15. N. Golyandina, V. Nekrutkin, and A. Zhigljavsky Analysis of Time Series Structure - SSA and Related Techniques 2001 Chapman & Hall/CRC Boca Raton, Florida pp. 13-78
16. R. Vautard, and M. Ghil Singular spectrum analysis in nonlinear dynamics, with applications to paleoclimatic time series Phys. D, Nonlinear Phenomena 35 1989 395 424
17. R. Vautard, P. Yiou, and M. Ghil Singular spectrum analysis: a toolkit for short noisy chaotic signals Phys. D, Nonlinear Phenomena 58 1992 95 126
18. R.A. Pasmanter Variational search of periodic motions in complex dynamical systems Chaos, Solitons Fractals 6 1995 447 454
19. W.J. Wang, J. Chen, and X.K. Wu The application of some non-linear methods in rotating machinery fault diagnosis Mech. Syst. Signal Proc. 15 2001 697 705
20. R.G. Silva, K.J. Baker, and S.J. Wilcox The adaptability of a tool wear monitoring system under changing cutting conditions Mech. Syst. Signal Proc. 14 2 2000 287 298
21. K. Jemielniak, L. Kwiatkowski, and P. Wrzosek Diagnosis of tool wear based on cutting forces and acoustic emission measures as inputs to a neural network J. Intell. Manuf. 9 1998 447 455
22. K. Niranjan Prasad, and B. Ramamoorthy Tool wear evaluation by stereo vision and prediction by artificial neural networks J. Mater. Process. Technol. 112 2001 43 52
23. S.K. Choudhury, V.K. Jain, and Ch.V.V. Rama Rao On-line monitoring of tool wear in turning using a neural network Int. J. Mach. Tools Manuf. 39 1999 489 504
24. S. Das, P.P. Bandyopadhyay, and A.B. Chattopadhyay Neural networks based tool wear monitoring in turning medium carbon steel using a coated carbide tool J. Mater. Process. Technol. 63 1997 187 192
25. T. Ozel, and A. Nadgir 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 Manuf. 42 2002 287 297
26. C. Scheffer, H. Kratz, P.S. Heyns, and F. Klocke Development of a tool wear-monitoring system for hard turning Int. J. Mach. Tools Manuf. 43 10 2003 973 985
27. R.K. Dutta, S. Paul, and A.B. Chattopadhyay Applicability of the back-propagation algorithm in tool condition monitoring for faster convergence J. Mater. Process. Technol. 98 2000 299 309
28. S.J. Qin, and B. Rajagopal Combining statistics and expert systems with neural networks for empirical process modelling Instrument. Control Int. Confer. Exhibition 48 1993 1711 1720
29. M. Rahman, Q. Zhou, and G.S. Hong On-line cutting state recognition in turning using a neural network Int. J. Adv. Manuf. Technol. 10 1995 87 92