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165 REFERENCES 1. Black, J.T., Flow stress model in metal cutting. Journal of Engineering for Industry, 1979. 101 (4): p. 403-415. 2. Thomas, T.R., Rough Surfaces. 2nd ed ed. 1999, London Imperial College Press. 3. Sick, B., On-Line 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, 2002. 16(4): p. 487-546. 4. Mitsui, K., In-proces sensors for surface roughness and their applications. Precision Engineering, 1986. 8(4): p. 212-220. 5. Shin, Y.C., Oh, S. J., & Coker, S. A, Surface roughness measurement by ultrasonic sensing for in-process monitoring. Journal of Engineering for Industry, 1995. 117: p. 439-447. 6. Cook, N.H., Prediction of tool life and optimal machining conditions. Wear, 1980. 62(1): p. 223-231. 7. Koren, Y., Adaptive control systems for machining. Manufacturing Review, 1989. 2(1): p. 6-15. 8. Kannatey-Asibu Jr, E. and D.A. Dornfeld, A study of tool wear using statistical analysis of metal-cutting acoustic emission. Wear, 1982. 76(2): p. 247-261. 9. Szecsi, T., Automatic cutting-tool condition monitoring on CNC lathes. Journal of Materials Processing Technology, 1998. 77(1-3): p. 64-69. 10. Shao, H., H.L. Wang, and X.M. Zhao, A cutting power model for tool wear monitoring in milling. International Journal of Machine Tools and Manufacture, 2004. 44(14): p. 1503-1509. 11. Shi, D. and N.N. Gindy, Development of an online machining process monitoring system: Application in hard turning. Sensors and Actuators A: Physical, 2007. 135(2): p. 405-414. 12. Salgado, D.R. and F.J. Alonso, An approach based on current and sound signals for in-process tool wear monitoring. International Journal of Machine Tools and Manufacture, 2007. 47(14): p. 2140-2152. 13. Byrne, G., et al., Tool Condition Monitoring (TCM) -- The Status of Research and Industrial Application. CIRP Annals - Manufacturing Technology, 1995. 44(2): p. 541-567. 14. Dornfeld, D., Application of acoustic emission techniques in manufacturing. NDT & E International, 1992. 25(6): p. 259-269. 15. Dornfeld, D.A. and M.F. DeVries, Neural Network Sensor Fusion for Tool Condition Monitoring. CIRP Annals - Manufacturing Technology, 1990. 39(1): p. 101-105. 16. Rangwala, S. and D. Dornfeld, A study of acoustic emission generated during orthogonal metal cutting--2: Spectral analysis. International Journal of Mechanical Sciences, 1991. 33(6): p. 489-499. 17. Rangwala, S. and D. Dornfeld, A study of acoustic emission generated during orthogonal metal cutting--1: Energy analysis. International Journal of Mechanical Sciences, 1991. 33(6): p. 471-487. 18. Rehorn, A.G., J. Jiang, and P.E. Orban, State-of-the-art methods and results in tool condition monitoring: a review. The International Journal of Advanced Manufacturing Technology, 2005. 26(7): p. 693-710. 19. Dinakaran, D., S. Sampathkumar, and N. Sivashanmugam, An experimental investigation on monitoring of crater wear in turning using ultrasonic technique.

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165

REFERENCES

1. Black, J.T., Flow stress model in metal cutting. Journal of Engineering forIndustry, 1979. 101 (4): p. 403-415.

2. Thomas, T.R., Rough Surfaces. 2nd ed ed. 1999, London Imperial College Press.3. Sick, B., On-Line and Indirect Tool Wear Monitoring in Turning with Artificial

Neural Networks: A Review of More Than a Decade of Research. MechanicalSystems and Signal Processing, 2002. 16(4): p. 487-546.

4. Mitsui, K., In-proces sensors for surface roughness and their applications.Precision Engineering, 1986. 8(4): p. 212-220.

5. Shin, Y.C., Oh, S. J., & Coker, S. A, Surface roughness measurement byultrasonic sensing for in-process monitoring. Journal of Engineering for Industry,1995. 117: p. 439-447.

6. Cook, N.H., Prediction of tool life and optimal machining conditions. Wear, 1980.62(1): p. 223-231.

7. Koren, Y., Adaptive control systems for machining. Manufacturing Review, 1989.2(1): p. 6-15.

8. Kannatey-Asibu Jr, E. and D.A. Dornfeld, A study of tool wear using statisticalanalysis of metal-cutting acoustic emission. Wear, 1982. 76(2): p. 247-261.

9. Szecsi, T., Automatic cutting-tool condition monitoring on CNC lathes. Journal ofMaterials Processing Technology, 1998. 77(1-3): p. 64-69.

10. Shao, H., H.L. Wang, and X.M. Zhao, A cutting power model for tool wearmonitoring in milling. International Journal of Machine Tools and Manufacture,2004. 44(14): p. 1503-1509.

11. Shi, D. and N.N. Gindy, Development of an online machining process monitoringsystem: Application in hard turning. Sensors and Actuators A: Physical, 2007.135(2): p. 405-414.

12. Salgado, D.R. and F.J. Alonso, An approach based on current and sound signalsfor in-process tool wear monitoring. International Journal of Machine Tools andManufacture, 2007. 47(14): p. 2140-2152.

13. Byrne, G., et al., Tool Condition Monitoring (TCM) -- The Status of Research andIndustrial Application. CIRP Annals - Manufacturing Technology, 1995. 44(2): p.541-567.

14. Dornfeld, D., Application of acoustic emission techniques in manufacturing. NDT& E International, 1992. 25(6): p. 259-269.

15. Dornfeld, D.A. and M.F. DeVries, Neural Network Sensor Fusion for ToolCondition Monitoring. CIRP Annals - Manufacturing Technology, 1990. 39(1): p.101-105.

16. Rangwala, S. and D. Dornfeld, A study of acoustic emission generated duringorthogonal metal cutting--2: Spectral analysis. International Journal ofMechanical Sciences, 1991. 33(6): p. 489-499.

17. Rangwala, S. and D. Dornfeld, A study of acoustic emission generated duringorthogonal metal cutting--1: Energy analysis. International Journal of MechanicalSciences, 1991. 33(6): p. 471-487.

18. Rehorn, A.G., J. Jiang, and P.E. Orban, State-of-the-art methods and results intool condition monitoring: a review. The International Journal of AdvancedManufacturing Technology, 2005. 26(7): p. 693-710.

19. Dinakaran, D., S. Sampathkumar, and N. Sivashanmugam, An experimentalinvestigation on monitoring of crater wear in turning using ultrasonic technique.

166

International Journal of Machine Tools and Manufacture, 2009. 49(15): p. 1234-1237.

20. Li, H., S. Wu, and H. Kratz, FFT and Wavelet-Based Analysis of the Influence ofMachine Vibrations on Hard Turned Surface Topographies. Tsinghua Science &Technology, 2007. 12(4): p. 441-446.

21. Dimla, D.E. and P.M. Lister, On-line metal cutting tool condition monitoring.: I:force and vibration analyses. International Journal of Machine Tools andManufacture, 2000. 40(5): p. 739-768.

22. Peng, Z.K., F.L. Chu, and P.W. Tse, Singularity analysis of the vibration signalsby means of wavelet modulus maximal method. Mechanical Systems and SignalProcessing, 2007. 21(2): p. 780-794.

23. Bonifacio, M.E.R. and A.E. Diniz, Correlating tool wear, tool life, surfaceroughness and tool vibration in finish turning with coated carbide tools. Wear,1994. 173(1-2): p. 137-144.

24. Yeo, S.H., L.P. Khoo, and S.S. Neo, Tool condition monitoring using reflectanceof chip surface and neural network. Journal of Intelligent Manufacturing, 2000.11(6): p. 507-514.

25. Zhang Y, Z.Z., Han Z Detection of tool breakage in turning operations usingneural networks, in International Conference on Intelligent Manufacturing. 1995,SPIE: Wuhan. p. 463

26. Rahman M, Z.Q., Hong GS, Application of Kohonen neural network for toolcondition monitoring, in International Conference on Intelligent Manufacturing,S.Y.J.Z.C.-G. Li, Editor. 1995, SPIE: Wuhan. p. 422.

27. Barschdorff, D., et al., Cutting tool monitoring in turning under varying cuttingconditions: an artificial neural network approach, in Proceedings of the 6thinternational conference on Industrial and engineering applications of artificialintelligence and expert systems. 1993, Gordon \& Breach Science Publishers:Edinburgh, Scotland. p. 353-359.

28. Bikramjit P, P.D., Mondal MS, Joarder R Prediction of power requirement inturning using a GA-fuzzy approach, in Soft computing and industry: recentapplications, M.K. Rajkumar Roy, Seppo Ovaska, Editor. 2002, Springer. p. 167.

29. Ingo Jossa , U.M., Wolf- Joachim Fischer, Application of the FSOM to machinevibration monitorin, in Advances in Soft Computing, M.W. Rainer Hampel,Nasredin Chaker, Editor. 2000, springer Verlag: Dresden University ofTechnology, Austria. p. 397.

30. U.Femmer, D.B.a., Artificial neural networks for wear estimation, in IntelligentManufacturing Systems IMS'94. 1994: Vienna. p. 151–155.

31. X. LI , A.Y.C.N., Monitoring cutting conditions for tool scheduling in CNCmachining. Manufacturing Systems, 1996. 25: p. 377–383.

32. Kamarthi, S.V. and S. Pittner, Fourier and Wavelet Transform for Flank WearEstimation -- A Comparison. Mechanical Systems and Signal Processing, 1997.11(6): p. 791-809.

33. Nee, X.L.a.A.Y.C., Monitoring cutting conditions for tool scheduling in CNCmachining. Manufacturing Systems 1996. 25: p. 377–383.

34. Elanayar, S.F.A.U.S.Y.C. and Y.C. Shin, Robust Tool Wear Estimation WithRadial Basis Function Neural Networks FAU - Elanayar Sunil. Transaction of theASME, 1995. 117.

167

35. Niu, Y., Y. Wong, and G. Hong, An intelligent sensor system approach forreliable tool flank wear recognition. The International Journal of AdvancedManufacturing Technology, 1998. 14(2): p. 77-84.

36. Dornfeld, A.S.a.D.A., On designing tool wear monitoring systems, in Proceedingsof the Joint Hungarian–British Mechatronics Conference. 1994 Budapest. p. 17–22.

37. Silva, R.G., Cutting tool condition monitoring of the turning process usingartificial intelligence. 1997, University of Glamorgan.

38. S. Liang, D.D., Tool wear detection using time series analysis of acousticemission. Journal of Engineering for Industry-Transactions of the ASME, 1989.111(3): p. 199- 205.

39. Ravindra, H.V., Y.G. Srinivasa, and R. Krishnamurthy, Acoustic emission for toolcondition monitoring in metal cutting. Wear, 1997. 212(1): p. 78-84.

40. Nayfeh, T.H., O.K. Eyada, and J.C. Duke, An integrated ultrasonic sensor formonitoring gradual wear on-line during turning operations. International Journalof Machine Tools and Manufacture, 1995. 35(10): p. 1385-1395.

41. Nayfeh, T.H., A direct on-line ultrasonic sensing method to determine tool andprocess conditions during turning operations. 1993, Virginia Technical Institute:Blacksburg.

42. Colgan J, C.H., Danai K, Hayasi S.R, On-line tool breakage detection in turning:a multi-sensor method. Vol. 116. 1994, New York, NY, ETATS-UNIS: AmericanSociety of Mechanical Engineers.

43. Tarng YS, L.C., Nian CY, An optimization approach for the fuzzy control ofturning operations, in 2nd New Zealand International Two-Stream Conference onArtificial Neural Networks and Expert Systems. 1995: Dunedin, New Zealand. p.145–149.

44. Masory, O. Detection of tool wear using multisensor readings defused by artificialneural network. in Applications of Artificial Neural Networks II,. 1991: SPIE.

45. Choudhury, S.K., V.K. Jain, and C.V.V. Rama Rao, On-line monitoring of toolwear in turning using a neural network. International Journal of Machine Toolsand Manufacture, 1999. 39(3): p. 489-504.

46. Choudhury, I.A. and M.A. El-Baradie, Surface roughness prediction in theturning of high-strength steel by factorial design of experiments. Journal ofMaterials Processing Technology, 1997. 67(1-3): p. 55-61.

47. Dan, L. and J. Mathew, Tool wear and failure monitoring techniques for turning--A review. International Journal of Machine Tools and Manufacture, 1990. 30(4):p. 579-598.

48. Choudhury, S.K. and K.K. Kishore, Tool wear measurement in turning usingforce ratio. International Journal of Machine Tools and Manufacture, 2000. 40(6):p. 899-909.

49. Sanjanwala, A., S.K. Choudhury, and V.K. Jain, On-line tool wear sensing andcompensation during turning operation. Precision Engineering, 1990. 12(2): p.81-84.

50. Choudhury, S.K. and S. Ramesh, On-line tool wear sensing and compensation inturning. Journal of Materials Processing Technology, 1995. 49(3-4): p. 247-254.

51. Giusti, F., M. Santochi, and G. Tantussi, On-Line Sensing of Flank and CraterWear of Cutting Tools. CIRP Annals - Manufacturing Technology, 1987. 36(1): p.41-44.

168

52. Jeon, J.U. and S.W. Kim, Optical flank wear monitoring of cutting tools by imageprocessing. Wear, 1988. 127(2): p. 207-217.

53. Bahr, B., S. Motavalli, and T. Arfi, Sensor fusion for monitoring machine toolconditions. International Journal of Computer Integrated Manufacturing, 1997.10(5): p. 314 - 323.

54. Jardine, A.K.S., D. Lin, and D. Banjevic, A review on machinery diagnostics andprognostics implementing condition-based maintenance. Mechanical Systems andSignal Processing, 2006. 20(7): p. 1483-1510.

55. S. Poyhonen, P.J., and H. Hyotyniemi Signal processing of vibrations forcondition monitoring of an induction motor, in First International Symposium onControl, Communications and Signal Processing. 2004: New York. p. 499-502.

56. Randall, R.B., A New Method of Modeling Gear Faults. Journal of MechanicalDesign, 1982. 104(2): p. 259-267.

57. Cooley, J. and J. Tukey, An Algorithm for the Machine Calculation of ComplexFourier Series. Mathematics of Computation, 1965. 19(90): p. 297-301.

58. Schulz H, S.H., Gebauer KP Model based diagnostics of machine tools and theturning process, in Working Conference on Process Planning for ComplexMachining with AI Methods. 1991: Gaussig, Germany.

59. Barker RW, K.R., Hinich MJ Monitoring rotating tool wear using higher-orderspectral features. Journal of Engineering for Industry-Transactions of the ASME,1993. 115: p. 23–29.

60. Li, X. and X.P. Guan, Time-frequency-analysis-based minor cutting edge fracturedetection during end milling. Mechanical Systems and Signal Processing, 2004.18(6): p. 1485-1496.

61. Dimla, D.E., The impact of cutting conditions on cutting forces and vibrationsignals in turning with plane face geometry inserts. Journal of MaterialsProcessing Technology, 2004. 155-156: p. 1708-1715.

62. Soman K. P., R.K.I., Insight into Wavelets- from Theory to Practice. 2005:Prentice Hall India Publishers.

63. Li, X., S. Dong, and Z. Yuan, Discrete wavelet transform for tool breakagemonitoring. International Journal of Machine Tools and Manufacture, 1999.39(12): p. 1935-1944.

64. Silva, R.G., et al., Tool Wear Monitoring of Turning Operations by NeuralNetwork and Expert System Classification of a Feature Set Generated fromMultiple Sensors. Mechanical Systems and Signal Processing, 1998. 12(2): p. 319-332.

65. Silva, R.G., et al., The Adaptability of a Tool Wear Monitoring system underchaning Cutting Conditions. Mechanical Systems and Signal Processing, 2000.14(2): p. 287-298.

66. Pearl, J., Evidential reasoning using stochastic simulation of causal models.Artificial Intelligence, 1987. 32(2): p. 245-257.

67. Quinlan, J.R., Induction of Decision Trees. Mach. Learn., 1986. 1(1): p. 81-106.68. Nir Friedman, D.G., MoisesGoldszmidt Bayesian Network Classifiers. Machine

Learning, 1997. 29: p. 131-163.69. Sun, J., et al., Multiclassification of tool wear with support vector machine by

manufacturing loss consideration. International Journal of Machine Tools andManufacture, 2004. 44(11): p. 1179-1187.

169

70. Cho, S., et al., Tool breakage detection using support vector machine learning ina milling process. International Journal of Machine Tools and Manufacture, 2005.45(3): p. 241-249.

71. Shi, D. and N.N. Gindy, Tool wear predictive model based on least squaressupport vector machines. Mechanical Systems and Signal Processing, 2007. 21(4):p. 1799-1814.

72. Achiche, S., et al., Tool wear monitoring using genetically-generated fuzzyknowledge bases. Engineering Applications of Artificial Intelligence. 15(3-4): p.303-314.

73. Chungchoo, C. and D. Saini, On-line tool wear estimation in CNC turningoperations using fuzzy neural network model. International Journal of MachineTools and Manufacture, 2002. 42(1): p. 29-40.

74. Kuo, R.J., Multi-sensor integration for on-line tool wear estimation throughartificial neural networks and fuzzy neural network. Engineering Applications ofArtificial Intelligence, 2000. 13(3): p. 249-261.

75. Kuo, R.J. and P.H. Cohen, Multi-sensor integration for on-line tool wearestimation through radial basis function networks and fuzzy neural network.Neural Networks, 1999. 12(2): p. 355-370.

76. Yao, Y. and X.D. Fang, Modelling of multivariate time series for tool wearestimation in finish-turning. International Journal of Machine Tools andManufacture, 1992. 32(4): p. 495-508.

77. Li, X., A brief review: acoustic emission method for tool wear monitoring duringturning. International Journal of Machine Tools and Manufacture, 2002. 42(2): p.157-165.

78. Zhu, K., Y.S. Wong, and G.S. Hong, Wavelet analysis of sensor signals for toolcondition monitoring: A review and some new results. International Journal ofMachine Tools and Manufacture, 2009. 49(7-8): p. 537-553.

79. A.L, K., The matrix of power spectra levels of turned surfaces roughness.International Journal of Machine Tool Design and Research, 1983. 23(2-3): p.161-167.

80. Thomas, M., et al., Effect of tool vibrations on surface roughness during lathe dryturning process. Computers & Industrial Engineering, 1996. 31(3-4): p. 637-644.

81. Lin, W.S., B.Y. Lee, and C.L. Wu, Modeling the surface roughness and cuttingforce for turning. Journal of Materials Processing Technology, 2001. 108(3): p.286-293.

82. Ho, S.-Y., et al., Accurate modeling and prediction of surface roughness bycomputer vision in turning operations using an adaptive neuro-fuzzy inferencesystem. International Journal of Machine Tools and Manufacture, 2002. 42(13): p.1441-1446.

83. Risbood, K.A., U.S. Dixit, and A.D. Sahasrabudhe, Prediction of surfaceroughness and dimensional deviation by measuring cutting forces and vibrationsin turning process. Journal of Materials Processing Technology, 2003. 132(1-3):p. 203-214.

84. Puertas Arbizu, I. and C.J. Luis Pérez, Surface roughness prediction by factorialdesign of experiments in turning processes. Journal of Materials ProcessingTechnology, 2003. 143-144: p. 390-396.

85. Pal, K. and D. Chakraborty, Surface roughness prediction in turning usingartificial neural network. Neural Comput. Appl., 2005. 14(4): p. 319-324.

170

86. Salgado, D., et al., In-process surface roughness prediction system using cuttingvibrations in turning. The International Journal of Advanced ManufacturingTechnology, 2009. 43(1): p. 40-51.

87. Benardos, P.G. and G.C. Vosniakos, Predicting surface roughness in machining:a review. International Journal of Machine Tools and Manufacture, 2003. 43(8): p.833-844.

88. Grzesik, W., A revised model for predicting surface roughness in turning. Wear,1996. 194(1-2): p. 143-148.

89. Lin, S.C. and M.F. Chang, A study on the effects of vibrations on the surface finishusing a surface topography simulation model for turning. International Journal ofMachine Tools and Manufacture, 1998. 38(7): p. 763-782.

90. Baek, D.K., T.J. Ko, and H.S. Kim, Optimization of feedrate in a face millingoperation using a surface roughness model. International Journal of MachineTools and Manufacture, 2001. 41(3): p. 451-462.

91. Chen, C.C.A., W.C. Liu, and N.A. Duffie, A surface topography model forautomated surface finishing. International Journal of Machine Tools andManufacture. 38(5-6): p. 543-550.

92. Kim, B.H. and C.N. Chu, Texture prediction of milled surfaces using texturesuperposition method. Computer-Aided Design, 1999. 31(8): p. 485-494.

93. Lee, K.Y., et al., Simulation of surface roughness and profile in high-speed endmilling. Journal of Materials Processing Technology, 2001. 113(1-3): p. 410-415.

94. Abouelatta, O.B. and J. Mádl, Surface roughness prediction based on cuttingparameters and tool vibrations in turning operations. Journal of MaterialsProcessing Technology, 2001. 118(1-3): p. 269-277.

95. Ghani, A.K., I.A. Choudhury, and Husni, Study of tool life, surface roughness andvibration in machining nodular cast iron with ceramic tool. Journal of MaterialsProcessing Technology, 2002. 127(1): p. 17-22.

96. Jang, D.Y., et al., Study of the correlation between surface roughness and cuttingvibrations to develop an on-line roughness measuring technique in hard turning.International Journal of Machine Tools and Manufacture, 1996. 36(4): p. 453-464.

97. Dhar, N.R., S. Paul, and A.B. Chattopadhyay, The influence of cryogenic coolingon tool wear, dimensional accuracy and surface finish in turning AISI 1040 andE4340C steels. Wear, 2001. 249(10-11): p. 932-942.

98. Muñoz-Escalona, P. and Z. Cassier, Influence of the critical cutting speed on thesurface finish of turned steel. Wear, 1998. 218(1): p. 103-109.

99. Thiele, J.D. and S. N. Melkote, Effect of cutting edge geometry and workpiecehardness on surface generation in the finish hard turning of AISI 52100 steel.Journal of Materials Processing Technology, 1999. 94(2-3): p. 216-226.

100. Baptista, R. and J.F. Antune Simões, Three and five axes milling of sculpturedsurfaces. Journal of Materials Processing Technology, 2000. 103(3): p. 398-403.

101. Coker, S.A. and Y.C. Shin, In-process control of surface roughness due to toolwear using a new ultrasonic system. International Journal of Machine Tools andManufacture, 1996. 36(3): p. 411-422.

102. Diniz, A.E. and J.C. Filho, Influence of the relative positions of tool andworkpiece on tool life, tool wear and surface finish in the face milling process.Wear, 1999. 232(1): p. 67-75.

103. Davim, J.P., A note on the determination of optimal cutting conditions for surfacefinish obtained in turning using design of experiments. Journal of MaterialsProcessing Technology, 2001. 116(2-3): p. 305-308.

171

104. Kopac, J. and M. Bahor, Interaction of the technological history of a workpiecematerial and the machining parameters on the desired quality of the surfaceroughness of a product. Journal of Materials Processing Technology, 1999. 92-93:p. 381-387.

105. Kopac, J., M. Bahor, and M. Sokovic, Optimal machining parameters forachieving the desired surface roughness in fine turning of cold pre-formed steelworkpieces. International Journal of Machine Tools and Manufacture, 2002.42(6): p. 707-716.

106. Alauddin, M., M.A. El Baradie, and M.S.J. Hashmi, Computer-aided analysis of asurface-roughness model for end milling. Journal of Materials ProcessingTechnology, 1995. 55(2): p. 123-127.

107. Alauddin, M., M.A. El Baradie, and M.S.J. Hashmi, Optimization of surface finishin end milling Inconel 718. Journal of Materials Processing Technology, 1996.56(1-4): p. 54-65.

108. Fuht, K.-H. and C.-F. Wu, A Proposed statistical model for surface qualityprediction in end-milling of A1 alloy. International Journal of Machine Tools andManufacture, 1995. 35(8): p. 1187-1200.

109. Mansour, A. and H. Abdalla, Surface roughness model for end milling: a semi-free cutting carbon casehardening steel (EN32) in dry condition. Journal ofMaterials Processing Technology, 2002. 124(1-2): p. 183-191.

110. Azouzi, R. and M. Guillot, On-line prediction of surface finish and dimensionaldeviation in turning using neural network based sensor fusion. InternationalJournal of Machine Tools and Manufacture, 1997. 37(9): p. 1201-1217.

111. Varghese, S. and V. Radharkrishnan, A multi sensor approach to in-processmonitoring of surface roughness. Journal of Materials Processing Technology,1994. 44(3-4): p. 353-362.

112. Chien, W.-T. and C.-Y. Chou, The predictive model for machinability of 304stainless steel. Journal of Materials Processing Technology, 2001. 118(1-3): p.442-447.

113. Suresh, P.V.S., P. Venkateswara Rao, and S.G. Deshmukh, A genetic algorithmicapproach for optimization of surface roughness prediction model. InternationalJournal of Machine Tools and Manufacture, 2002. 42(6): p. 675-680.

114. Lee, B.Y., Y.S. Tarng, and H.R. Lii, An investigation of modeling of themachining database in turning operations. Journal of Materials ProcessingTechnology, 2000. 105(1-2): p. 1-6.

115. Li, X.P., K. Iynkaran, and A.Y.C. Nee, A hybrid machining simulator based onpredictive machining theory and neural network modelling. Journal of MaterialsProcessing Technology, 1999. 89-90: p. 224-230.

116. Matsumura, T., H. Sekiguchi, and E. Usui, An evaluation approach of machinetool characteristics with adaptive prediction. Journal of Materials ProcessingTechnology, 1996. 62(4): p. 440-447.

117. Zain, A.M., H. Haron, and S. Sharif, Prediction of surface roughness in the endmilling machining using Artificial Neural Network. Expert Systems withApplications, 2010. 37(2): p. 1755-1768.

118. Özel, T. and Y. Karpat, Predictive modeling of surface roughness and tool wearin hard turning using regression and neural networks. International Journal ofMachine Tools and Manufacture, 2005. 45(4-5): p. 467-479.

172

119. E. Daniel Kirby, Z.Z.a.J.C.C., Development of an Accelerometer based SurfaceRoughness Prediction System in Turning Operation using Multiple RegressionTechniques. Journal of Industrial Technology, 2004. 20(4): p. 408-412.

120. Sahin, Y. and A.R. Motorcu, Surface Roughness Prediction Model in Machiningof Carbon Steel by PVD Coated Cutting Tools. American Journal of AppliedSciences. 1(1): p. 12-17.

121. B. Sidda Reddy, J.S.K., K. Vijaya Kumar Reddy Prediction of Surface Roughnessin Turning Using Adaptive Neuro-Fuzzy Inference System Jordan Journal ofMechanical and Industrial Engineering 2009. 3(4): p. 252-259.

122. Matsumura, T., et al., Autonomous turning operation planning with adaptiveprediction of tool wear and surface roughness. Journal of Manufacturing Systems,1993. 12(3): p. 253-262.

123. Grzesik, W. and S. Brol, Hybrid approach to surface roughness evaluation inmultistage machining processes. Journal of Materials Processing Technology,2003. 134(2): p. 265-272.

124. Chen, M.L.H.D.J.C., A Multiple Regression Model to Predict In-process SurfaceRoughness in Turning Operation Via Accelerometer. February, 2001. 17(2).

125. Villalobos, L. and S. Gruber, Measurement of surface roughness parameter usinga neural network and laser scattering. Industrial Metrology, 1991. 2(1): p. 33-44.

126. Lela, B., D. Bajić, and S. Jozić, Regression analysis, support vector machines,and Bayesian neural network approaches to modeling surface roughness in facemilling. The International Journal of Advanced Manufacturing Technology, 2009.42(11): p. 1082-1088.

127. Sun, W., J. Chen, and J. Li, Decision tree and PCA-based fault diagnosis ofrotating machinery. Mechanical Systems and Signal Processing, 2007. 21(3): p.1300-1317.

128. Elangovan, M., et al., Evaluation of expert system for condition monitoring of asingle point cutting tool using principle component analysis and decision treealgorithm. Expert Systems with Applications, 2011. 38(4): p. 4450-4459.

129. Zadeh, L.A., Making computers think like people. IEEE. Spectrum, 1984. 8: p. 26-32.

130. Cox, E., The Fuzzy Systems Handbook - A Practitioner's Guide to Building,Using, and Maintaining Fuzzy Systems. 1994, Academic Press

131. Elangovan, M., et al., Effect of SVM kernel functions on classification of vibrationsignals of a single point cutting tool. Expert Systems with Applications, 2011.38(12): p. 15202-15207.

132. Vapnik, V., Statistical Learning Theory. 1998: Wiley-Interscience.133. Cortes, C. and V. Vapnik, Support-vector networks. Machine Learning, 1995.

20(3): p. 273-297.134. Vapnik, V.N., An overview of statistical learning theory. Neural Networks, IEEE

Transactions, 1999. 10, : p. 988 - 999.135. Widodo, A. and B.-S. Yang, Support vector machine in machine condition

monitoring and fault diagnosis. Mechanical Systems and Signal Processing, 2007.21(6): p. 2560-2574.

136. Rao, S.B., Tool Wear Monitoring Through the Dynamics of Stable Turning.ASME Journal of Engg. for Industry, 1986. 108: p. 183-190.

137. Randall, J.G., Cutting apparatus, U.S.P. 5024563, Editor. June 18, 1991: US.138. Elangovan, M., K.I. Ramachandran, and V. Sugumaran, Studies on Bayes

classifier for condition monitoring of single point carbide tipped tool based on

173

statistical and histogram features. Expert Systems with Applications, 2010. 37(3):p. 2059-2065.

139. Xiaoli, L. and Y. Zhejun, Tool wear monitoring with wavelet packet transform--fuzzy clustering method. Wear, 1998. 219(2): p. 145-154.

140. Mathworks, MatLab user manual. 2011b: Natick, Massachusetts, U.S.A.141. He, Z., X. Chen, and Q. Qian, A study of wavelet entropy measure definition and

its application for fault feature pick-up and classification. Journal of Electronics(China), 2007. 24(5): p. 628-634.

142. Bayrak, M., The effect of cutting conditions on surface roughness and comparisonwith expert system, in Department of Mechanical Engineering. 2002, GaziUniversity: Ankara, Turkey.

143. Hasan Gokkaya, M.N., The Effects of Cutting Tool Coating on the SurfaceRoughness of AISI 1015 Steel Depending on Cutting Parameters. Turkish Journalof Engineering and Environmental Sciences, 2006. 30(5): p. 307-316.

144. Sykes, A., An introduction to regression analysis. 1993: Law School, Universityof Chicago.

145. Tsao, C., Comparison between response surface methodology and radial basisfunction network for core-center drill in drilling composite materials. TheInternational Journal of Advanced Manufacturing Technology, 2008. 37(11): p.1061-1068.

146. Garg, S., S. Pal, and D. Chakraborty, Evaluation of the performance ofbackpropagation and radial basis function neural networks in predicting the drillflank wear. Neural Computing & Applications, 2007. 16(4): p. 407-417.