FUZZY RADIAL BASIS FUNCTION (FRBF) NETWORK BASED TOOL CONDITION MONITORING SYSTEM USING VIBRATION SIGNALS

REFERENCES

• Abu-Mahfouz , I. ( 2003 ) Drilling wear detection and classification using vibration signals and artificial neural network . International Journal of Machine Tools and Manufacture , 43 : 707 – 720 .
   Web of Science ®Google Scholar
• Abu-Mahfouz , I. ( 2005 ) Drill flank wear estimation using supervised vector quantization neural networks . Neural Computing and Applications , 14 ( 3 ): 167 – 175 .
   Google Scholar
• Barker , R.W. ; Klutke , G. ; Hinich , M.J. ( 1993 ) Monitoring rotating tool wear using higher order spectral features . Journal of Engineering for Industry, Transactions of the ASME , 115 : 23 – 29 .
   Google Scholar
• Broomhead , D.S. ; Lowe , D. (1988) Multivariable functional interpolation and adaptive networks. Complex Systems , 2: 321–355.
   Google Scholar
• Bugmaan , G. ( 1998 ) Normalized Gaussian radial basis function networks . Neurocomputing , 20 : 97 – 110 .
   Google Scholar
• Byrne , G. ; Dornfeld , D. ; Inasaki , I. ; Ketteler , G. ; Teti , R. ( 1995 ) Tool condition monitoring (TCM) – the status of research and industrial application . Annals of the CIRP , 44 ( 2 ): 541 – 567 .
   Google Scholar
• Coifman , R.R. ; Wickerhauser , M.V. ( 1992 ) Entropy-based algorithms for best basis selection , IEEE Transaction on Information Theory , 38 ( 2 ): 713 – 718 .
   Web of Science ®Google Scholar
• Dimla , D.E. ; Lister , P.M. ; Leighton , N.J. ( 1997 ) Neural network solutions to the tool condition monitoring problem in metal cutting – a critical review of methods . International Journal of Machine Tools and Manufacture , 37 ( 9 ): 1219 – 1241 .
   Web of Science ®Google Scholar
• Dimla , D.E. ( 2002 ) The correlation of vibration signal features to cutting tool wear in a metal turning operation . International Journal of Advanced Manufacturing Technology , 19 : 705 – 713 .
   Google Scholar
• Dimla Snr , D.E. ( 2000 ) Sensor signals for tool-wear monitoring in metal cutting operations – a review of methods . International Journal of Machine Tools and Manufacture , 40 ( 8 ): 1073 – 1098 .
   Google Scholar
• El-Wardany , T.I. ; Gao , D. ; Elbestawi , M.A. ( 1996 ) Tool condition monitoring in drilling using vibration signature analysis . International Journal of Machine Tools and Manufacture , 36 ( 6 ): 687 – 711 .
   Web of Science ®Google Scholar
• Garg , S. ; Pal , S.K. ; Chakraborty , D. ( 2007 ) Evaluation of the performance of back propagation and radial basis function neural networks in predicting the drill flank wear . Neural Computing and Application , 16 : 407 – 417 .
   Google Scholar
• Jantunen , E. ( 2002 ) A summary of methods applied to tool condition monitoring in drilling . International Journal of Machine Tools and Manufacture , 42 : 997 – 1010 .
   Web of Science ®Google Scholar
• Jang , J.-S.R. ; Sun , C.-T. ; Mizutani , E. ( 1997 ) Neuro-fuzzy and soft computing – a computational approach to learning and machine intelligence . Prentice Hall of India Private Limited , pp. 423 – 427 .
   Google Scholar
• Jiang , C.Y. ; Zhang , Y.Z. ; Xu , H.J. ( 1987 ) In-process monitoring of tool wear stage by the frequency band-energy method . Annals of the CIRP , 36 ( 1 ): 45 – 48 .
   Google Scholar
• Lee , H. ; Cho , D. ( 2006 ) Calculation of reference cutting force as a criterion of rough milling using FEM analysis . Materials Science Forum , 526 : 43 – 48 .
   Google Scholar
• Li , X. ; Dong , S. ; Venuvinod , P.K. ( 2000 ) Hybrid learning for tool wear monitoring . International Journal of Advanced Manufacturing Technology , 16 : 303 – 307 .
   Web of Science ®Google Scholar
• Mathworks , Inc. ( 2004 ) Wavelet toolbox, MATLAB 7.0. .
   Google Scholar
• Mitra , S. ; Basak , J. ( 2001 ) FRBF: A fuzzy radial basis function network . Neural Computing and Applications , 10 : 244 – 252 .
   Google Scholar
• Orhan , S. ; Ali , O.Er. ; Camuscu , N. ; Aslan , E. ( 2007 ) Tool wear evaluation by vibration analysis during end milling of AISI D3 cold work tool steel with 35 HRC hardness . NDT and E International , 40 ( 2 ): 121 – 126 .
   Google Scholar
• Pal , S.K. ; Mitra , S. ( 1992 ) Multilayer perceptron, fuzzy sets, and classification . IEEE Transaction on Neural Networks , 3 ( 5 ): 683 – 697 .
   PubMed Web of Science ®Google Scholar
• Patra , K. ; Pal , S.K. ; Bhattacharyya , K. ( 2007a ) Artificial neural network based prediction of drill flank wear from motor current signals . Applied Soft Computing , 7 : 929 – 935 .
   Google Scholar
• Patra , K. ; Pal , S.K. ; Bhattacharyya , K. ( 2007b ) Application of wavelet packet transform in drill wear monitoring . Machining Science and Technology , 11 ( 3 ): 413 – 432 .
   Web of Science ®Google Scholar
• Patra , K. ( 2008 ) Study on Different Strategies for Soft Computing-Based Drill Wear Monitoring Using Multiple Sensors , Ph.D Thesis , Central library , IIT Kharagpur .
   Google Scholar
• Patra , K. ; Pal , S.K. ; Bhattacharyya , K. ( 2009 ) Application of wavelet packet transform based normalized radial function network in a machining process , International Journal of Materials and Product Technology , 35 ( 1/2 ): 184 – 198 .
   Google Scholar
• Scheffer , C. ; Heyns , P.S. ( 2001 ) Wear monitoring in turning operations using vibration and strain measurements . Mechanical Systems and Signal Processing , 15 ( 6 ): 1185 – 1202 .
   Web of Science ®Google Scholar
• 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 , 6 ( 4 ): 487 – 546 .
   Google Scholar
• Sun , Q. ; Tang , Y. ; Lu , W.Y. ; Ji , Y. ( 2005 ) Feature extraction with discrete wavelet transform for drill wear monitoring . Journal of Vibration and Control , 11 ( 11 ): 1375 – 1396 .
   Google Scholar
• Wu , Y. ; Du , R. ( 1996 ) Feature extraction and assessment using wavelet packets for monitoring of machining processes . Mechanical Systems and Signal Processing , 10 ( 1 ): 29 – 53 .
   Web of Science ®Google Scholar
• Zhu , K. ; Wong , Y.S. ; Hong , G.S. ( 2009 ) Wavelet analysis of sensor signals for tool condition monitoring: a review and some new results . International Journal of Machine Tools and Manufacture , doi: doi:10.1016/j.ijmachtools.2009.02.003 .
   Google Scholar