Skip to main content
SpringerLink
Log in

Digital Image Processing in Machining

  • Chapter
  • First Online:
Modern Mechanical Engineering

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

Abstract

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.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Watanabe R, Mizuno Y, Kawasaki A (1999) Determination of non-uniform sintering shrinkage in MIM powder compacts by digital image processing. Met Mater 5:163–169

    Google Scholar 

  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 1854–1859

    Google Scholar 

  3. Feiste, KL Reichert C, Reimche W, Stegemann D (1998) Process integrated detection and characterization of casting defects. NDT.net. http://www.ndt.net/article/ecndt98/steel/348/348.htm. Accessed 27 April 2013

  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 36:484–489

    Google Scholar 

  5. Balfour C, Smith JS, Amin-Nejad S (2004) Feature correlation for weld image-processing applications. Int J Prod Res 42:975–995

    Google Scholar 

  6. Lee RS, Hsu QC (1994) Image-processing system for circular-grid analysis in sheet-metal forming. Exp Mech 34:108–115

    Google Scholar 

  7. O’Leary P (2005) Machine vision for feedback control in a steel rolling mill. Comput Ind 56:997–1004

    Google Scholar 

  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. http://dx.doi.org/10.1016/j.cirpj.2013.02.005

  9. Smith GT (2008) Cutting tool technology industrial handbook. Springer, London

    Google Scholar 

  10. Teti R, Jemielniak K, O’Donnell G, Dornfeld D (2010) Advanced monitoring of machining operations. CIRP Ann Manuf Technol 59:717–739

    Google Scholar 

  11. Tool-life testing with single-point turning tools, ISO3685:1993

    Google Scholar 

  12. Abellan-Nebot JV, Subirón FR (2010) A review of machining monitoring systems based on artificial intelligence process models. Int J Adv Manuf Technol 47:237–257

    Google Scholar 

  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 26:693–710

    Google Scholar 

  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 132:041015–1–041015-16

    Google Scholar 

  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 44:541–567

    Google Scholar 

  16. Prickett PW, Johns C (1999) An overview of approaches to end milling tool monitoring. Int J Mach Tool Manuf 39:105–122

    Google Scholar 

  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 40:1073–1098

    Google Scholar 

  18. Jemielniak K (1999) Commercial tool condition monitoring systems. Int J Adv Manuf Technol 15:711–721

    Google Scholar 

  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 1:97–102

    Google Scholar 

  20. Al-Kindi G, Zughaer H (2012) An approach to improved CNC machining using vision-based system. Mater Manuf Process 27:765–774

    Google Scholar 

  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 36:458–466

    Google Scholar 

  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. http://ieeexplore.ieee.org/iel4/5851/15628/00722834.pdf. Accessed 25 Feb 2013

  23. McBride J, Maul C (2004) The 3D measurement and analysis of high precision surfaces using confocal optical methods. IEICE Trans Electron E87-C:1261–1267

    Google Scholar 

  24. Abouelatta OB (2010) 3D Surface roughness measurement using a light sectioning vision system. World Congress Engineering. http://www.iaeng.org/publication/WCE2010/WCE2010_pp698-703.pdf. Accessed 25 Feb 2013

  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 44:611–619

    Google Scholar 

  26. Jantunen E (2002) A summary of methods applied to tool condition monitoring in drilling. Int J Mach Tool Manuf 42:997–1010

    Google Scholar 

  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 21:1250005–1250019

    Google Scholar 

  28. Jähne B (2002) Digital image processing. Springer, Heidelberg

    MATH  Google Scholar 

  29. Burke MW (1996) Image acquisition: handbook of machine vision engineering. Chapman and Hall, London

    Google Scholar 

  30. Weis W (1993) Tool Wear Measurement on basis of optical sensors, vision systems and neuronal networks (application milling). IEEE Xplore. http://ieeexplore.ieee.org. Accessed 4 March 2010

  31. Kurada S, Bradley C (1997) A machine vision system for tool wear assessment. Tribol Int 30:295–304

    Google Scholar 

  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 130–131:668–674

    Google Scholar 

  33. Wang WH, Hong GS, Wong YS (2005) Flank wear measurement by successive image analysis. Comput Ind 56:816–830

    Google Scholar 

  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 46:199–207

    Google Scholar 

  35. Pfeifer T, Wiegers L (2000) Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28:209–218

    Google Scholar 

  36. Jurkovic J, Korosec M, Kopac J (2005) New approach in tool wear measuring technique using CCD vision system. Int J Mach Tool Manuf 45:1023–1030

    Google Scholar 

  37. Kerr D, Pengilley J, Garwood R (2006) Assessment and visualisation of machine tool wear using computer vision. Int J Adv Manuf Technol 28:781–791

    Google Scholar 

  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, UK

    Google Scholar 

  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, Berlin

    Google Scholar 

  40. Tsai DM, Chen JJ, Chert JF (1998) A vision system for surface roughness assessment using neural networks. Int J Adv Manuf Technol 14:412–422

    Google Scholar 

  41. Bradley C, Wong YS (2001) Surface texture indicators of tool wear—a machine vision approach. Int J Adv Manuf Technol 17:435–443

    Google Scholar 

  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 45:228–234

    Google Scholar 

  43. Dhanasekar B, Ramamoorthy B (2008) Assessment of surface roughness based on super resolution reconstruction algorithm. Int J Adv Manuf Technol 35:1191–1205

    Google Scholar 

  44. Gonzalez RC, Woods RE (2002) Digital image processing. Prentice-Hall Inc, Upper Saddle River

    Google Scholar 

  45. Sortino M (2003) Application of statistical filtering for optical detection of tool wear. Int J Mach Tool Manuf 43:493–497

    Google Scholar 

  46. Alegre E, Barreiro J, Fernandez RA, Castejn M (2006) Design of a computer vision system to estimate tool wearing. Mater Sci Forum 526:61–66

    Google Scholar 

  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 38:718–727

    Google Scholar 

  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 43:11–21

    Google Scholar 

  49. Yang M, Kwon O (1996) Crater wear measurement using computer vision and automatic focusing. J Mater Process Technol 58:362–367

    Google Scholar 

  50. Yang M, Kwon O (1998) A tool condition recognition system using image processing. Control Eng Pract 6:1389–1395

    Google Scholar 

  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 40:342–348

    Google Scholar 

  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, Germany

    Google Scholar 

  53. Dhanasekar B, Krishna MN, Bhaduri B, Ramamoorthy B (2008) Evaluation of surface roughness based on monochromatic speckle correlation using image processing. Precis Eng 32:196–206

    Google Scholar 

  54. Nakao Y (2001) Measurement of drilling burr by image processing technique. ASPE. http://www.aspe.net/publications/Annual_2001/PDF/POSTERS/METRO/FORM/1142.PDF. Accessed 6 July 2010

  55. Fadare DA, Oni AO (2009) Development and application of a machine vision system for measurement of tool wear. ARPN J Eng Appl Sci 4:42–49

    Google Scholar 

  56. Josso B, Burton DR, Lalor MJ (2001) Wavelet strategy for surface roughness analysis and characterisation. Comput Methods Appl Mech Eng 191:829–842

    MATH  Google Scholar 

  57. Josso B, Burton DR, Lalor MJ (2002) Frequency normalised wavelet transform for surface roughness analysis and characterisation. Wear 252:491–500

    Google Scholar 

  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 164–165:1410–1415

    Google Scholar 

  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 Kong

    Google Scholar 

  60. Zawada-Tomkiewicz A (2010) Estimation of surface roughness parameter based on machined surface image. Metrol Meas Syst XVII:493–504

    Google Scholar 

  61. Dhanasekar B, Ramamoorthy B (2010) Restoration of blurred images for surface roughness evaluation using machine vision. Tribol Int 43:268–276

    Google Scholar 

  62. Yoon H, Chung SC (2004) Vision inspection of micro-drilling processes on the machine tool. Trans NAMRI/SME 32:391–394

    Google Scholar 

  63. Liang YT, Chiou YC, Louh CJ (2005) Automatic wear measurement of Ti-based coatings milling via image registration. IAPRConference. http://www.mva-org.jp/Proceedings/CommemorativeDVD/2005/papers/2005088.pdf. Accessed 25 Feb 2013

  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 43:55–60

    Google Scholar 

  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 220:1409–1415

    Google Scholar 

  66. Kassim AA, Mannan MA, Jing M (2000) Machine tool condition monitoring using workpiece surface texture analysis. Mach Vision Appl 11:257–263

    Google Scholar 

  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 21:969–979

    MATH  Google Scholar 

  68. Mannan MA, Mian Z, Kassim AA (2004) Tool wear monitoring using a fast Hough transform of images of machined surfaces. Mach Vis Appl 15:156–163

    Google Scholar 

  69. Kassim AA, Mian Z, Mannan MA (2004) Connectivity oriented fast Hough transform for tool wear monitoring. Pattern Recogn 37:1925–1933

    Google Scholar 

  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 133:041003-1–041003-7

    Google Scholar 

  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 26:575–598

    Google Scholar 

  72. Lanzetta M (2001) A new flexible high-resolution vision sensor for tool condition monitoring. J Mater Process Technol 119:73–82

    Google Scholar 

  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 44:823–829

    Google Scholar 

  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 21:667–676

    Google Scholar 

  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, China

    Google Scholar 

  76. Galante G, Piacentini M, Ruisi VF (1991) Surface roughness detection by tool image processing. Wear 148:211–220

    Google Scholar 

  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 115:37–43

    Google Scholar 

  78. Sharan RV, Onwubolu GC (2011) Measurement of end-milling burr using image processing techniques. Proc I Mech Eng B—J Eng Manuf 225:448–452

    Google Scholar 

  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 47:1847–1853

    Google Scholar 

  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 48:1005–1013

    Google Scholar 

  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 15:3–12

    Google Scholar 

  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, Singapore

    Google Scholar 

  83. Damodarasamy S, Raman S (1991) Texture analysis using computer vision. Comput Ind 16:25–34

    Google Scholar 

  84. Hoy DEP, Yu F (1991) Surface quality assessment using computer vision methods. J Mater Process Technol 28:265–274

    Google Scholar 

  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 30:241–253

    Google Scholar 

  86. Cuthbert L, Huynh V (1992) Statistical analysis of optical Fourier transform patterns for surface texture measurement. Meas Sci Technol 3:740–745

    Google Scholar 

  87. Ramamoorthy B, Radhakrishnan V (1993) Statistical approaches to surface texture classification. Wear 167:155–161

    Google Scholar 

  88. Wong FS, Nee AFC, Li XQ, Reisdorj C (1997) Tool condition monitoring using laser scatter pattern. J Mater Process Technol 63:205–210

    Google Scholar 

  89. Gupta M, Raman S (2001) Machine vision assisted characterization of machined surfaces. Int J Prod Res 39:759–784

    Google Scholar 

  90. Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tool Manuf 41:1251–1263

    Google Scholar 

  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 42:1441–1446

    Google Scholar 

  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 19:295–301

    Google Scholar 

  93. Lee BY, Yu SF, Juan H (2004) The model of surface roughness inspection by vision system in turning. Mechatronics 14:129–141

    Google Scholar 

  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 29:95–100

    Google Scholar 

  95. Arunachalam N, Ramamoorthy B (2007) Texture analysis for grinding wheel wear assessment using machine vision. Proc Inst Mech Eng B—J Eng Manuf 221:419–430

    Google Scholar 

  96. Khalifa OO, Densibali A, Faris W (2006) Image processing for chatter identification in machining processes. Int J Adv Manuf Technol 31:443–449

    Google Scholar 

  97. Akbari AA, Fard AM, Chegini AG (2006) An effective image based surface roughness estimation approach using neural network. IEEEXplore. http://ieeexplore.ieee.org/xpls/abs_all.jsp?arnumber=4259888&tag=1. Accessed 25 Feb 2013

  98. Al-Kindi GA, Shirinzadeh B (2007) An evaluation of surface roughness parameters measurement using vision-based data. Int J Mach Tool Manuf 47:697–708

    Google Scholar 

  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 37:92–103

    Google Scholar 

  100. Ravikumar S, Ramachandran KI, Sugumaran V (2011) Machine learning approach for automated visual inspection of machine components. Exp Syst Appl 38:3260–3266

    Google Scholar 

  101. Haralick RM, Shanmugam K, Dinsten I (1973) Textural features for image classification. IEEE Trans Syst SMC-3:610–621

    Google Scholar 

  102. Jain R, Kasturi R, Schunck BG (1995) Machine vision. McGraw-Hill, London

    Google Scholar 

  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, London

    Google Scholar 

  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 365:38–43

    Google Scholar 

  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 222:1395–1404

    Google Scholar 

  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 223:791–800

    Google Scholar 

  107. Gadelmawla ES (2011) Estimation of surface roughness for turning operations using image texture features. Proc Inst Mech Eng B—J Eng 225:1281–1292

    Google Scholar 

  108. Galloway MM (1975) Texture analysis using gray level run lengths. Comput Graphics 4:172–179

    Google Scholar 

  109. Ramana KV, Ramamoorthy B (1996) Statistical methods to compare the texture features of machined surfaces. Pattern Recogn 29:1447–1459

    Google Scholar 

  110. Kassim AA, Mannan MA, Mian Z (2007) Texture analysis methods for tool condition monitoring. Image Vis Comput 25:1080–1090

    Google Scholar 

  111. Russ JC (1998) Fractal dimension measurement of engineering surfaces. Int J Mach Tool Manuf 38:567–571

    Google Scholar 

  112. Kang MC, Kim JS, Kim KH (2005) Fractal dimension analysis of machined surface depending on coated tool wear. Surf Coat Technol 193:259–265

    Google Scholar 

  113. Karthik A, Chandra S, Ramamoorthy B, Das S (1997) 3D tool wear measurement and visualisation using stereo imaging. Int J Mach Tool Manuf 37:1573–1581

    Google Scholar 

  114. Prasad KN, Ramamoorthy B (2001) Tool wear evaluation by stereo vision and prediction by artificial neural network. J Mater Process Technol 112:43–52

    Google Scholar 

  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, Vienna

    Google Scholar 

  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, Brazil

    Google Scholar 

  117. Wang WH, Wong YS, Hong GS (2006) 3D measurement of crater wear by phase shifting method. Wear 261:164–171

    Google Scholar 

  118. Devillez A, Lesko S, Mozerc W (2004) Cutting tool crater wear measurement with white light interferometry. Wear 256:56–65

    Google Scholar 

  119. Dawson TG, Kurfess TR (2005) Quantification of tool wear using white light interferometry and three-dimensional computational metrology. Int J Mach Tool Manuf 45:591–596

    Google Scholar 

  120. Ng KW, Moon KS (2001) Measurement of 3-D tool wear based on focus error and micro-coordinate measuring system. http://aspe.net/publications/Annual_2001/pdf/posters/metro/instrum/1207.pdf. Accessed on 27 Feb 2013

  121. Liang YT, Chiou YC (2006) An effective drilling wear measurement based on visual inspection technique. www.atlantis-press.com/php/download_paper.php?id=267. Accessed 26 Feb 2013

  122. Su JC, Huang CK, Tarng YS (2006) An automated flank wear measurement of microdrills using machine vision. J Mater Process Technol 180:328–335

    Google Scholar 

  123. Yasui H, Haraki Y, Sakata M (2001) Development of automatic image processing system for evaluation of wheel surface condition in ultra-smoothness grinding. http://aspe.net/publications/Annual_2001/pdf/papers/pfab/1191.pdf. Accessed 28 Feb 2013

  124. Heger T, Pandit M (2004) Optical wear assessment for grinding tools. J Electron Imag 13:450–461

    Google Scholar 

  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 54:1033–1042

    Google Scholar 

  126. Schmähling J, Hamprecht FA, Hoffmann DMP (2006) A three-dimensional measure of surface roughness based on mathematical morphology. Int J Mach Tool Manuf 46:1764–1769

    Google Scholar 

  127. Senin N, Ziliotti M, Groppetti R (2007) Three-dimensional surface topography segmentation through clustering. Wear 262:395–410

    Google Scholar 

  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 28:1567–1585

    Google Scholar 

  129. Dhanasekar B, Ramamoorthy B (2006) Evaluation of surface roughness using a image processing and machine vision system. MAPAN—J Metrol Soc I 21:9–15

    Google Scholar 

  130. Narayanan MR, Gowri S, Krishna MM (2007) On line surface roughness measurement using image processing and machine vision. http://www.iaeng.org/publication/WCE2007/WCE2007_pp645-647.pdf. Accessed 28 Feb 2013

  131. Gadelmawla ES (2004) A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT E Int 37:577–588

    Google Scholar 

  132. Priya P, Ramamoorthy B (2010) Machine vision for surface roughness assessment of inclined components. Key Eng Mater 437:141–144

    Google Scholar 

  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 17:327–336

    Google Scholar 

  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 3:304–316

    Google Scholar 

  135. Tsai DM, Wu SK (2000) Automated surface inspection using Gabor filters. Int J Adv Manuf Technol 16:474–482

    Google Scholar 

  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 46:59–69

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Precision Engineering and Metrology Lab, CSIR-Central Mechanical Engineering Research Institute, Durgapur, 713209, India

    Samik Dutta & Ranjan Sen

  2. Mechanical Engineering Department, Indian Institute of Technology, Kharagpur, 721302, India

    Surjya K. Pal

Authors
  1. Samik Dutta
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Surjya K. Pal
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Ranjan Sen
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Surjya K. Pal .

Editor information

Editors and Affiliations

  1. Department of Mechanical Engineering, University of Aveiro, Aveiro, Portugal

    J. Paulo Davim

Rights and permissions

Reprints and permissions

Copyright information

© 2014 Springer-Verlag Berlin Heidelberg

About this chapter

Cite this chapter

Dutta, S., Pal, S.K., Sen, R. (2014). Digital Image Processing in Machining. In: Davim, J. (eds) Modern Mechanical Engineering. Materials Forming, Machining and Tribology. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-45176-8_13

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-45176-8_13

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-45175-1

  • Online ISBN: 978-3-642-45176-8

  • eBook Packages: EngineeringEngineering (R0)

Publish with us

Policies and ethics

Search

Navigation