Digital Image Processing in Machining

Part of the Materials Forming, Machining and Tribology book series (MFMT)


This chapter speaks about the application of digital image processing in conventional machining. Advantages and disadvantages of digital image processing techniques over the other sensors used in machining for product quality improvement is also discussed here. A short introduction to image processing techniques used in machining is presented here. A detailed review of image processing applications in machining for over the past decade is discussed in this chapter. Also, an example of an image texture analysis method utilized for cutting tool condition detection through machined surface images is presented. An overall conclusion leading to future work required in this field has been mentioned.


  1. 1.
    Watanabe R, Mizuno Y, Kawasaki A (1999) Determination of non-uniform sintering shrinkage in MIM powder compacts by digital image processing. Met Mater 5:163–169Google Scholar
  2. 2.
    Fernandez C, Platero C, Campoy P, Aracil R (1993) Vision system for on-line surface inspection in aluminum casting process. In: Proceedings of IEEE international conference on industrial electronics, control, instrumentation and automation, vol 3, pp 1854–1859Google Scholar
  3. 3.
    Feiste, KL Reichert C, Reimche W, Stegemann D (1998) Process integrated detection and characterization of casting defects. Accessed 27 April 2013
  4. 4.
    Sinha P, Muthukumaran S, Sivakumar R, Mukherjee SK (2008) Condition monitoring of first mode of metal transfer in friction stir welding by image processing techniques. Int J Adv Manuf Technol 36:484–489Google Scholar
  5. 5.
    Balfour C, Smith JS, Amin-Nejad S (2004) Feature correlation for weld image-processing applications. Int J Prod Res 42:975–995Google Scholar
  6. 6.
    Lee RS, Hsu QC (1994) Image-processing system for circular-grid analysis in sheet-metal forming. Exp Mech 34:108–115Google Scholar
  7. 7.
    O’Leary P (2005) Machine vision for feedback control in a steel rolling mill. Comput Ind 56:997–1004Google Scholar
  8. 8.
    Dutta S, Pal SK, Mukhopadhyay S, Sen R (2013) Application of digital image processing in tool condition monitoring: A review. CIRP J Manuf Sci Technol.
  9. 9.
    Smith GT (2008) Cutting tool technology industrial handbook. Springer, LondonGoogle Scholar
  10. 10.
    Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739Google Scholar
  11. 11.
    Tool-life testing with single-point turning tools, ISO3685:1993Google Scholar
  12. 12.
    Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47:237–257Google Scholar
  13. 13.
    Rehorn AG, Jiang J, Orban PE (2005) State-of-the-art methods and results in tool condition monitoring: a review. Int J Adv Manuf Technol 26:693–710Google Scholar
  14. 14.
    Roth JT, Djurdjanovic D, Yang X, Mears L, Kurfess T (2010) Quality and inspection of machining operations- tool condition monitoring. J Manuf Sci Eng—Trans ASME 132:041015–1–041015-16Google Scholar
  15. 15.
    Byrne G, Dornfeld D, lnasaki I, Ketteler G, Konig W, Teti R (1995) Tool condition monitoring (TCM)—the status of research and industrial application. CIRP Ann Manuf Technol 44:541–567Google Scholar
  16. 16.
    Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tool Manuf 39:105–122Google Scholar
  17. 17.
    Dimla E, Dimla S (2000) Sensor signals for tool-wear monitoring in metal cutting operations—a review of methods. Int J Mach Tool Manuf 40:1073–1098Google Scholar
  18. 18.
    Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15:711–721Google Scholar
  19. 19.
    Jemielniak K, Arrazola PJ (2008) Application of AE and cutting force signals in tool condition monitoring in micro-milling. CIRP J Manuf Sci Technol 1:97–102Google Scholar
  20. 20.
    Al-Kindi G, Zughaer H (2012) An approach to improved CNC machining using vision-based system. Mater Manuf Process 27:765–774Google Scholar
  21. 21.
    Dutta S, Datta A, Chakladar ND, Pal SK et al (2012) Detection of tool condition from the turned surface images using an accurate grey level co-occurrence technique. Precis Eng 36:458–466Google Scholar
  22. 22.
    Okada S, Imade M, Miyauchi H et al (1998) 3-D shape measurement of free-form machined surfaces by optical ring imaging system. IEEE Xplore. Accessed 25 Feb 2013
  23. 23.
    McBride J, Maul C (2004) The 3D measurement and analysis of high precision surfaces using confocal optical methods. IEICE Trans Electron E87-C:1261–1267Google Scholar
  24. 24.
    Abouelatta OB (2010) 3D Surface roughness measurement using a light sectioning vision system. World Congress Engineering. Accessed 25 Feb 2013
  25. 25.
    Demircioglu P, Durakbasa MN (2011) Investigations on machined metal surfaces through the stylus type and optical 3D instruments and their mathematical modelling with the help of statistical techniques. Measurement 44:611–619Google Scholar
  26. 26.
    Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tool Manuf 42:997–1010Google Scholar
  27. 27.
    Liu W, Zheng X, Liu S, Jia Z (2012) A roughness measurement method based on genetic algorithm and neural network for microheterogeneous surface in deep-hole parts. J Circuit Syst Comp 21:1250005–1250019Google Scholar
  28. 28.
    Jähne B (2002) Digital image processing. Springer, HeidelbergMATHGoogle Scholar
  29. 29.
    Burke MW (1996) Image acquisition: handbook of machine vision engineering. Chapman and Hall, LondonGoogle Scholar
  30. 30.
    Weis W (1993) Tool Wear Measurement on basis of optical sensors, vision systems and neuronal networks (application milling). IEEE Xplore. Accessed 4 March 2010
  31. 31.
    Kurada S, Bradley C (1997) A machine vision system for tool wear assessment. Tribol Int 30:295–304Google Scholar
  32. 32.
    Kim JH, Moon DK, Lee DW et al (2002) Tool wear measuring technique on the machine using CCD and exclusive jig. J Mater Proc Technol 130–131:668–674Google Scholar
  33. 33.
    Wang WH, Hong GS, Wong YS (2005) Flank wear measurement by successive image analysis. Comput Ind 56:816–830Google Scholar
  34. 34.
    Wang WH, Hong GS, Wong YS (2006) Flank wear measurement by a threshold independent method with sub-pixel accuracy. Int J Mach Tool Manuf 46:199–207Google Scholar
  35. 35.
    Pfeifer T, Wiegers L (2000) Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28:209–218Google Scholar
  36. 36.
    Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tool Manuf 45:1023–1030Google Scholar
  37. 37.
    Kerr D, Pengilley J, Garwood R (2006) Assessment and visualisation of machine tool wear using computer vision. Int J Adv Manuf Technol 28:781–791Google Scholar
  38. 38.
    Makki H, Heinemann RK, Hinduja S, Owodunni OO (2009) Online determination of tool run-out and wear using machine vision and image processing techniques. In: Innovative production machines and systems. Cardiff University, Wales, UKGoogle Scholar
  39. 39.
    Alegre E, Barreiro J, Castejón M, Suarez S (2008) Computer vision and classification techniques on the surface finish control in machining processes. In: Campilho A, Kamel M (eds) Image analysis and recognition (LNCS 5112). Springer, BerlinGoogle Scholar
  40. 40.
    Tsai DM, Chen JJ, Chert JF (1998) A vision system for surface roughness assessment using neural networks. Int J Adv Manuf Technol 14:412–422Google Scholar
  41. 41.
    Bradley C, Wong YS (2001) Surface texture indicators of tool wear—a machine vision approach. Int J Adv Manuf Technol 17:435–443Google Scholar
  42. 42.
    Kumar R, Kulashekar P, Dhanasekar B, Ramamoorthy B (2005) Application of digital image magnification for surface roughness evaluation using machine vision. Int J Mach Tool Manuf 45:228–234Google Scholar
  43. 43.
    Dhanasekar B, Ramamoorthy B (2008) Assessment of surface roughness based on super resolution reconstruction algorithm. Int J Adv Manuf Technol 35:1191–1205Google Scholar
  44. 44.
    Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall Inc, Upper Saddle RiverGoogle Scholar
  45. 45.
    Sortino M (2003) Application of statistical filtering for optical detection of tool wear. Int J Mach Tool Manuf 43:493–497Google Scholar
  46. 46.
    Alegre E, Barreiro J, Fernandez RA, Castejn M (2006) Design of a computer vision system to estimate tool wearing. Mater Sci Forum 526:61–66Google Scholar
  47. 47.
    Sahabi HH, Ratnam MM (2008) On-line monitoring of tool wear in turning operation in the presence of tool misalignment. Int J Adv Manuf Technol 38:718–727Google Scholar
  48. 48.
    Sahabi HH, Ratnam MM (2009) Assessment of flank wear and nose radius wear from workpiece roughness profile in turning operation using machine vision. Int J Adv Manuf Technol 43:11–21Google Scholar
  49. 49.
    Yang M, Kwon O (1996) Crater wear measurement using computer vision and automatic focusing. J Mater Process Technol 58:362–367Google Scholar
  50. 50.
    Yang M, Kwon O (1998) A tool condition recognition system using image processing. Control Eng Pract 6:1389–1395Google Scholar
  51. 51.
    Zhongxiang H, Lei Z, Jiaxu T, Xuehong M, Xiaojun S (2009) Evaluation of three-dimensional surface roughness parameters based on digital image processing. Int J Adv Manuf Technol 40:342–348Google Scholar
  52. 52.
    Stemmer M, Pavim A, Adur M et al (2005) Machine vision and neural networks applied to wear classification on cutting tools. In: Proceedings of the EOS conference on industrial imaging and machine vision, Munich, GermanyGoogle Scholar
  53. 53.
    Dhanasekar B, Krishna MN, Bhaduri B, Ramamoorthy B (2008) Evaluation of surface roughness based on monochromatic speckle correlation using image processing. Precis Eng 32:196–206Google Scholar
  54. 54.
    Nakao Y (2001) Measurement of drilling burr by image processing technique. ASPE. Accessed 6 July 2010
  55. 55.
    Fadare DA, Oni AO (2009) Development and application of a machine vision system for measurement of tool wear. ARPN J Eng Appl Sci 4:42–49Google Scholar
  56. 56.
    Josso B, Burton DR, Lalor MJ (2001) Wavelet strategy for surface roughness analysis and characterisation. Comput Methods Appl Mech Eng 191:829–842MATHGoogle Scholar
  57. 57.
    Josso B, Burton DR, Lalor MJ (2002) Frequency normalised wavelet transform for surface roughness analysis and characterisation. Wear 252:491–500Google Scholar
  58. 58.
    Niola VN, Quaremba G (2005) A problem of emphasizing features of a surface roughness by means the Discrete Wavelet Transform. J Mater Process Technol 164–165:1410–1415Google Scholar
  59. 59.
    Li PY, Hao CY, Zhu SW (2007) Machining tools wear condition detection based on wavelet packet. In: Proceedings of the sixth international conference on machine learning and cybernetics, Hong KongGoogle Scholar
  60. 60.
    Zawada-Tomkiewicz A (2010) Estimation of surface roughness parameter based on machined surface image. Metrol Meas Syst XVII:493–504Google Scholar
  61. 61.
    Dhanasekar B, Ramamoorthy B (2010) Restoration of blurred images for surface roughness evaluation using machine vision. Tribol Int 43:268–276Google Scholar
  62. 62.
    Yoon H, Chung SC (2004) Vision inspection of micro-drilling processes on the machine tool. Trans NAMRI/SME 32:391–394Google Scholar
  63. 63.
    Liang YT, Chiou YC, Louh CJ (2005) Automatic wear measurement of Ti-based coatings milling via image registration. IAPRConference. Accessed 25 Feb 2013
  64. 64.
    Inoue S, Konishi M, Imai J (2009) Surface defect inspection of a cutting tool by image processing with neural networks. Mem Fac Eng—Okayama Univ 43:55–60Google Scholar
  65. 65.
    Atli AV, Urhan O, Ertürk S, Sönmez M (2006) A computer vision-based fast approach to drilling tool condition monitoring. Proc Inst Mech Eng B—J Eng Manuf 220:1409–1415Google Scholar
  66. 66.
    Kassim AA, Mannan MA, Jing M (2000) Machine tool condition monitoring using workpiece surface texture analysis. Mach Vision Appl 11:257–263Google Scholar
  67. 67.
    Mannan MA, Kassim AA, Jing M (2000) Application of image and sound analysis techniques to monitor the condition of cutting tools. Pattern Recogn Lett 21:969–979MATHGoogle Scholar
  68. 68.
    Mannan MA, Mian Z, Kassim AA (2004) Tool wear monitoring using a fast Hough transform of images of machined surfaces. Mach Vis Appl 15:156–163Google Scholar
  69. 69.
    Kassim AA, Mian Z, Mannan MA (2004) Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recogn 37:1925–1933Google Scholar
  70. 70.
    Bamberger H, Ramachandran S, Hong E, Katz R (2011) Identification of machining chatter marks on surfaces of automotive valve seats. J Manuf Sci Eng—Trans ASME 133:041003-1–041003-7Google Scholar
  71. 71.
    Oguamanam DCD, Raafat IH, Taboun SM (1994) A machine vision system for wear monitoring and breakage detection of single-point cutting tools. Comput Ind Eng 26:575–598Google Scholar
  72. 72.
    Lanzetta M (2001) A new flexible high-resolution vision sensor for tool condition monitoring. J Mater Process Technol 119:73–82Google Scholar
  73. 73.
    Lachance S, Bauer R, Warkentin A (2004) Application of region growing method to evaluate the surface condition of grinding wheels. Int J Mach Tool Manuf 44:823–829Google Scholar
  74. 74.
    Duan G, Chen YW, Sukegawa T (2010) Automatic optical flank wear measurement of microdrills using level set for cutting plane segmentation. Mach Vis Appl 21:667–676Google Scholar
  75. 75.
    Xiong G, Liu J, Avila A (2011) Cutting tool wear measurement by using active contour model based image processing. In: Proceedings of the 2011 IEEE international conference on mechatronics and automation, Beijing, ChinaGoogle Scholar
  76. 76.
    Galante G, Piacentini M, Ruisi VF (1991) Surface roughness detection by tool image processing. Wear 148:211–220Google Scholar
  77. 77.
    Park JJ, Ulsoy AJ (1993) On-line flank wear estimation using an adaptive observer and computer vision, Part 2: Experiment. J Eng Ind—Trans ASME 115:37–43Google Scholar
  78. 78.
    Sharan RV, Onwubolu GC (2011) Measurement of end-milling burr using image processing techniques. Proc I Mech Eng B—J Eng Manuf 225:448–452Google Scholar
  79. 79.
    Castejon M, Alegre E, Barreiro J, Hernandez LK (2007) On-line tool wear monitoring using geometric descriptors from digital images. Int J Mach Tool Manuf 47:1847–1853Google Scholar
  80. 80.
    Barreiro J, Castejon M, Alegre E, Hernandez LK (2008) Use of descriptors based on moments from digital images for tool wear monitoring. Int J Mach Tool Manuf 48:1005–1013Google Scholar
  81. 81.
    Alegre E, Rodríguez RA, Barreiro J, Ruiz J (2009) Use of contour signatures and classification methods to optimize the tool life in metal machining. Estonian J Eng 15:3–12Google Scholar
  82. 82.
    Tuceryan M, Jain AK (1998) Texture analysis. In: Chen CH, Pau LF, Wang PSP (eds) The handbook of pattern recognition and computer vision. World Scientific, SingaporeGoogle Scholar
  83. 83.
    Damodarasamy S, Raman S (1991) Texture analysis using computer vision. Comput Ind 16:25–34Google Scholar
  84. 84.
    Hoy DEP, Yu F (1991) Surface quality assessment using computer vision methods. J Mater Process Technol 28:265–274Google Scholar
  85. 85.
    Al-Kindi GA, Banl RM, Gilt KF (1992) An application of machine vision in the automated inspection of engineering surface. Int J Prod Res 30:241–253Google Scholar
  86. 86.
    Cuthbert L, Huynh V (1992) Statistical analysis of optical Fourier transform patterns for surface texture measurement. Meas Sci Technol 3:740–745Google Scholar
  87. 87.
    Ramamoorthy B, Radhakrishnan V (1993) Statistical approaches to surface texture classification. Wear 167:155–161Google Scholar
  88. 88.
    Wong FS, Nee AFC, Li XQ, Reisdorj C (1997) Tool condition monitoring using laser scatter pattern. J Mater Process Technol 63:205–210Google Scholar
  89. 89.
    Gupta M, Raman S (2001) Machine vision assisted characterization of machined surfaces. Int J Prod Res 39:759–784Google Scholar
  90. 90.
    Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tool Manuf 41:1251–1263Google Scholar
  91. 91.
    Ho SY, Lee KC, Chen SS, Ho SJ (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tool Manuf 42:1441–1446Google Scholar
  92. 92.
    Lee BY, Juan H, Yu SF (2002) A study of computer vision for measuring surface roughness in the turning process. Int J Adv Manuf Technol 19:295–301Google Scholar
  93. 93.
    Lee BY, Yu SF, Juan H (2004) The model of surface roughness inspection by vision system in turning. Mechatronics 14:129–141Google Scholar
  94. 94.
    Lee KC, Ho SJ, Ho SY (2005) Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precis Eng 29:95–100Google Scholar
  95. 95.
    Arunachalam N, Ramamoorthy B (2007) Texture analysis for grinding wheel wear assessment using machine vision. Proc Inst Mech Eng B—J Eng Manuf 221:419–430Google Scholar
  96. 96.
    Khalifa OO, Densibali A, Faris W (2006) Image processing for chatter identification in machining processes. Int J Adv Manuf Technol 31:443–449Google Scholar
  97. 97.
    Akbari AA, Fard AM, Chegini AG (2006) An effective image based surface roughness estimation approach using neural network. IEEEXplore. Accessed 25 Feb 2013
  98. 98.
    Al-Kindi GA, Shirinzadeh B (2007) An evaluation of surface roughness parameters measurement using vision-based data. Int J Mach Tool Manuf 47:697–708Google Scholar
  99. 99.
    Elango V, Karunamoorthy L (2008) Effect of lighting conditions in the study of surface roughness by machine vision—an experimental design approach. Int J Adv Manuf Technol 37:92–103Google Scholar
  100. 100.
    Ravikumar S, Ramachandran KI, Sugumaran V (2011) Machine learning approach for automated visual inspection of machine components. Exp Syst Appl 38:3260–3266Google Scholar
  101. 101.
    Haralick RM, Shanmugam K, Dinsten I (1973) Textural features for image classification. IEEE Trans Syst SMC-3:610–621Google Scholar
  102. 102.
    Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, LondonGoogle Scholar
  103. 103.
    Davies ER (2008) Introduction to texture analysis. In: Mirmedi M, Xie X, Suri J (eds) Handbook of texture analysis, 1st edn. Imperial College Press, LondonGoogle Scholar
  104. 104.
    Datta A, Dutta S, Pal SK, Sen R, Mukhopadhyay S (2012) Texture analysis of turned surfaces using grey level co-occurrence technique. Adv Mater Res 365:38–43Google Scholar
  105. 105.
    Gadelmawla ES, Eladawi AE, Abouelatta OB, Elewa IM (2008) Investigation of the cutting conditions in milling operations using image texture features. Proc Inst Mech Eng B—J Eng Manuf 222:1395–1404Google Scholar
  106. 106.
    Gadelmawla ES, Eladawi AE, Abouelatta OB, Elewa IM (2009) Application of computer vision for the prediction of cutting conditions in milling operations. Proc Inst Mech Eng B—J Eng Manuf 223:791–800Google Scholar
  107. 107.
    Gadelmawla ES (2011) Estimation of surface roughness for turning operations using image texture features. Proc Inst Mech Eng B—J Eng 225:1281–1292Google Scholar
  108. 108.
    Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graphics 4:172–179Google Scholar
  109. 109.
    Ramana KV, Ramamoorthy B (1996) Statistical methods to compare the texture features of machined surfaces. Pattern Recogn 29:1447–1459Google Scholar
  110. 110.
    Kassim AA, Mannan MA, Mian Z (2007) Texture analysis methods for tool condition monitoring. Image Vis Comput 25:1080–1090Google Scholar
  111. 111.
    Russ JC (1998) Fractal dimension measurement of engineering surfaces. Int J Mach Tool Manuf 38:567–571Google Scholar
  112. 112.
    Kang MC, Kim JS, Kim KH (2005) Fractal dimension analysis of machined surface depending on coated tool wear. Surf Coat Technol 193:259–265Google Scholar
  113. 113.
    Karthik A, Chandra S, Ramamoorthy B, Das S (1997) 3D tool wear measurement and visualisation using stereo imaging. Int J Mach Tool Manuf 37:1573–1581Google Scholar
  114. 114.
    Prasad KN, Ramamoorthy B (2001) Tool wear evaluation by stereo vision and prediction by artificial neural network. J Mater Process Technol 112:43–52Google Scholar
  115. 115.
    Otto T, Kurik L, Papstel J (2003) A digital measuring module for tool wear estimation. In: Katalinic B (ed) DAAAM international scientific book 2003. DAAAM International, ViennaGoogle Scholar
  116. 116.
    Schmitt R, Hermes R, Stemmer M et al (2005) Machine vision prototype for flank wear measurement on milling tools. In: 38th CIRP manufacturing systems seminar, Florianopolis, BrazilGoogle Scholar
  117. 117.
    Wang WH, Wong YS, Hong GS (2006) 3D measurement of crater wear by phase shifting method. Wear 261:164–171Google Scholar
  118. 118.
    Devillez A, Lesko S, Mozerc W (2004) Cutting tool crater wear measurement with white light interferometry. Wear 256:56–65Google Scholar
  119. 119.
    Dawson TG, Kurfess TR (2005) Quantification of tool wear using white light interferometry and three-dimensional computational metrology. Int J Mach Tool Manuf 45:591–596Google Scholar
  120. 120.
    Ng KW, Moon KS (2001) Measurement of 3-D tool wear based on focus error and micro-coordinate measuring system. Accessed on 27 Feb 2013
  121. 121.
    Liang YT, Chiou YC (2006) An effective drilling wear measurement based on visual inspection technique. Accessed 26 Feb 2013
  122. 122.
    Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180:328–335Google Scholar
  123. 123.
    Yasui H, Haraki Y, Sakata M (2001) Development of automatic image processing system for evaluation of wheel surface condition in ultra-smoothness grinding. Accessed 28 Feb 2013
  124. 124.
    Heger T, Pandit M (2004) Optical wear assessment for grinding tools. J Electron Imag 13:450–461Google Scholar
  125. 125.
    Palani S, Natarajan U (2011) Prediction of surface roughness in CNC end milling by machine vision system using artificial neural network based on 2D Fourier transform. Int J Adv Manuf Technol 54:1033–1042Google Scholar
  126. 126.
    Schmähling J, Hamprecht FA, Hoffmann DMP (2006) A three-dimensional measure of surface roughness based on mathematical morphology. Int J Mach Tool Manuf 46:1764–1769Google Scholar
  127. 127.
    Senin N, Ziliotti M, Groppetti R (2007) Three-dimensional surface topography segmentation through clustering. Wear 262:395–410Google Scholar
  128. 128.
    Sarma PMMS, Karunamoorthy L, Palanikumar K (2009) Surface roughness parameters evaluation in machining GFRP composites by PCD tool using digital image processing. J Reinf Plast Comp 28:1567–1585Google Scholar
  129. 129.
    Dhanasekar B, Ramamoorthy B (2006) Evaluation of surface roughness using a image processing and machine vision system. MAPAN—J Metrol Soc I 21:9–15Google Scholar
  130. 130.
    Narayanan MR, Gowri S, Krishna MM (2007) On line surface roughness measurement using image processing and machine vision. Accessed 28 Feb 2013
  131. 131.
    Gadelmawla ES (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E Int 37:577–588Google Scholar
  132. 132.
    Priya P, Ramamoorthy B (2010) Machine vision for surface roughness assessment of inclined components. Key Eng Mater 437:141–144Google Scholar
  133. 133.
    Kassim AA, Mian Z, Mannan MA (2006) Tool condition classification using Hidden Markov Model based on fractal analysis of machined surface textures. Mach Vis Appl 17:327–336Google Scholar
  134. 134.
    Vesselenyi T, Dzitac I, Dzitac S, Vaida V (2008) Surface roughness image analysis using quasi-fractal characteristics and fuzzy clustering methods. Int J Comput Comm Control 3:304–316Google Scholar
  135. 135.
    Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16:474–482Google Scholar
  136. 136.
    Zhang X, Krewet C, Kuhlenkötter B (2006) Automatic classification of defects on the product surface in grinding and polishing. Int J Mach Tool Manuf 46:59–69Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.Precision Engineering and Metrology LabCSIR-Central Mechanical Engineering Research InstituteDurgapurIndia
  2. 2.Mechanical Engineering DepartmentIndian Institute of TechnologyKharagpurIndia

Personalised recommendations