Open Access
Issue |
Mechanics & Industry
Volume 15, Number 6, 2014
|
|
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Page(s) | 497 - 507 | |
DOI | https://doi.org/10.1051/meca/2014052 | |
Published online | 26 September 2014 |
- G. Byrne, D. Dornfeld, I. Inasaki, G. Ketteler, W. König, R. Teti, Tool Condition Monitoring (TCM) – The Status of Research and Industrial Application, CIRP Annals: Manuf. Technol. 44 (1995) 541–567 [Google Scholar]
- S.H. Yeo, L.p. Khoo, S.S. Neo, Tool condition monitoring using reflectance of chip surface and neural network, J. Intelligent Manuf. 11 (2000) 507–514 [CrossRef] [Google Scholar]
- S. Kurada, C. Bradeley, A review of machine vision sensors for tool condition monitoring, Comput. Ind. 34 (1997) 55–72 [CrossRef] [Google Scholar]
- A.G. Rehorn, J. Jiang, P.E. Orban, State-of-the-art methods and results in tool condition monitoring: a review, Int. J. Adv. Manuf. Technol. 26 (2005) 693–710 [CrossRef] [Google Scholar]
- R.L. Kegg, On-line machine and process diagnostics, CIRP Annals: Manuf. Technol. 33 (1984) 469–473 [Google Scholar]
- S.N. Huang, K.K. Tan, Y.S. Wong, C.W. de Silva, H.L. Goh, W.W. Tan, Tool wear detection and fault diagnosis based on cutting force monitoring, Int. J. Mach. Tools Manuf. 47 (2007) 444–451 [Google Scholar]
- E. Kuljanic, M. SortinoTWEM, a method based on cutting forces–monitoring tool wear in face milling, Int. J. Mach. Tools Manuf. 45 (2005) 29–34 [CrossRef] [Google Scholar]
- F.J. Alonso, D.R. Salgado, Analysis of the structure of vibration signals for tool wear detection, Mech. Syst. Signal Process. 22 (2008) 735–748 [Google Scholar]
- S. Orhan, A. Osman, N. Camuscu, E. Aslan, Tool wear evaluation by vibration analysis during milling of AISI D3 cold work tool steel with 35 HRC hardness, NDTE&E Int. 40 (2007) 11–126 [Google Scholar]
- D.E. Dimla, Sensor signals for tool-wear monitoring in metal cutting operations–a review of methods, Int. J. Mach. Tools Manuf. 40 (2000) 1073–1098 [Google Scholar]
- J. Lin, Inverse estimation of the tool-work interface temperature in end milling, Int. J. Mach. Tools Manuf. 35 (1995) 751–760 [CrossRef] [Google Scholar]
- Srinivasa Pai P., Ramakrishna Rao P.K., Acoustic emission analysis for tool wear monitoring in face milling, Int. J. Prod. Res. 40 (2002) 1081–1093 [CrossRef] [Google Scholar]
- W.A. Gardner, Stationarizable Random Processes, IEEE Trans. Inf. Theory 24 (1978) 8–22 [CrossRef] [Google Scholar]
- W.A. Gardner, Statistical Spectral analysis: a non probabilistic theory, Prentice, Hall Inc, 1988 [Google Scholar]
- C. Capdessus, M. Sidahmed, J.L. Lacoume, Cyclostationary processes, application in gear faults early diagnosis, Mech. Syst. Signal Process. 14 (2000) 371–38 [Google Scholar]
- J. Antoni, F. Bonnardot, A. Raad, M. Elbadaoui, Cyclostationnary modelling of rotating machine vibration signals, Mech. Syst. Signal Process. 18 (2004) 1285–1314 [Google Scholar]
- J. Antoni, Apports de l’échantillonnage angulaire et de la cyclostationnarité au diagnostic par analyse vibratoire des moteurs thermiques, Thèse de l’Institut national polytechnique de Grenoble, 2000 [Google Scholar]
- J. Antoni, Cyclic spectral analysis in practice, Mech. Syst. Signal Process. 21 (2007) 597–630 [CrossRef] [Google Scholar]
- M.A. Elbestawi, T.A. Papazariou, R.X. Du, In process monitoring of tool wear in milling using cutting force signature, Int. J. Mach. Tools Manuf. 31 (1991) 55–73 [CrossRef] [Google Scholar]
- F. Bonnardot, Comparaison entre les analyses angulaire et temporelle des signaux vibratoires de machines tournantes. Etude du concept de cyclostationnarité floue, Thèse de l’Institut national polytechnique de Grenoble, 2004 [Google Scholar]
- R.B. Randall, J. Antoni, The relationship between spectral correlation and envelope analysis in the diagnostics of bearing faults and other cyclostationary machine signals, Mech. Syst. Signal Process. 15 (2001) 945–962 [Google Scholar]
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