Advertisement

Surface Roughness Evaluation of Milled Surfaces by Image Processing of Speckle and White-Light Images

  • J. Mahashar AliEmail author
  • H. Siddhi Jailani
  • M. Murugan
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)

Abstract

An experimental approach for surface roughness measurement based on the speckle images caused by a laser beam on the milled surfaces and the white-light images of the same surfaces is presented. Since the surface slope at every point of the surface influences the speckle pattern, the surface roughness parameters Rda and Rdq were used for comparison. A CMOS camera, LASER and LED light sources were used for capturing speckle and white-light images of the milled surfaces. From the image pixel intensity matrix, a signal vector was generated and was used for the image metric. It is found that standard deviation and mean of the image signal vector correlate well with Ra, Rda and Rdq values measured by a standard Taylor Hobson surface roughness tester. The correlation was found to be better for speckle images than the white-light images.

Keywords

Surface roughness Machining Vision system Image processing Speckle images Statistical parameters 

References

  1. 1.
    Hamed, A.M., El-Ghandoor, H., El-Diasty, F., Saudy, M.: Analysis of speckle images to assess surface roughness. Opt. Laser Technol. 36, 249–253 (2004)Google Scholar
  2. 2.
    Sodhi, M.S., Tiliouine, K.: Surface roughness monitoring using computer vision. Int. J. Mach. Tools Manufact. 36(7), 817–828 (1996)Google Scholar
  3. 3.
    Ma, L., Tan, T., Zhang, D.: Efficient iris recognition by characterizing key local variations. IEEE Trans. Image Process. 13, 739–750 (2004)CrossRefGoogle Scholar
  4. 4.
    Dhanasekar, B., Mohan, N.K., Bhaduri, B., Ramamoorthy, B.: Evaluation of surface roughness based on monochromatic speckle correlation using image processing. Precis. Eng. 32, 196–206 (2008)CrossRefGoogle Scholar
  5. 5.
    Jiang, X.J., Whitehouse, D.J.: Technological shifts in surface metrology. CIRP Ann. Manufact. Technol. 61, 815–836 (2012)Google Scholar
  6. 6.
    Zhao, X.Z., Zhao, G.: Surface roughness measurement using spatial-average analysis of objective speckle pattern in specular direction. Opt. Lasers Eng. 47, 1307–1316 (2009)CrossRefGoogle Scholar
  7. 7.
    Kumar, R., Kulashekar, P., Dhanasekar, B., Ramamoorthy, B.: Application of digital image magnification for surface roughness evaluation using machine vision. Int. J. Mach. Tools Manufact. 45, 228–234 (2005)CrossRefGoogle Scholar
  8. 8.
    Al-kindi, G.A., Shirinzadeh, B.:An evaluation of surface roughness parameters measurement using vision based data. Int. J. Mach. Tools Manufact. 47, 697–708 (2007)Google Scholar
  9. 9.
    Kiran, M.B., Ramamoorthy, B., Radhakrishnan, V.: Evaluation of surface roughness by vision system. Int. J. Mach. Tools Manufact. 38(5–6), 685–690 (1998)CrossRefGoogle Scholar
  10. 10.
    Priya, P., Ramamoorthy, B.: The influence of component inclination on surface finish evaluation using digital image processing. Int. J. Mach. Tools Manufact. 47, 570–579 (2007)CrossRefGoogle Scholar
  11. 11.
    Shahabi, H.H., Ratnam, M.M.: Prediction of surface roughness and dimensional deviation of workpiece in turning: a machine vision approach. Int. J. Manufact. Technol. 48, 213–226 (2010)Google Scholar
  12. 12.
    Leonard, L.C., Toal, V.: Roughness measurement of metallic surfaces based on the laser speckle contrast method. Opt. Lasers Eng. 30, 433–440 (1998)Google Scholar
  13. 13.
    Persson, U.: Surface roughness measurement on machined surfaces using angular speckle correlation. J. Mater. Process. Technol. 180, 233–238 (2006)Google Scholar
  14. 14.
    Jeyapoovan, T., Murugan, M., Clemend Bovas, B.: Statistical analysis of surface roughness measurements using laser speckle images. In: World Congress on Information and Communication Technologies, pp 378–382 (2012)Google Scholar
  15. 15.
    Gadelmawala, E.S., Koura, M.M.: Roughness parameters. J. Mater. Process. Technol. 123, 133–145(2002)Google Scholar
  16. 16.
    Ali, J.M., Murugan, M.: Surface roughness characterisation of turned surfaces using image processing. Int. J. Mach. Mach. Mater. 19(4), 394–406 (2017)Google Scholar
  17. 17.
    Gonzalez, R.C., Woods, R.E.: Digital Image Processing. Prentice Hall (2007)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • J. Mahashar Ali
    • 1
    Email author
  • H. Siddhi Jailani
    • 1
  • M. Murugan
    • 2
  1. 1.Department of Mechanical EngineeringB. S. Abdur Rahman Crescent Institute of Science and TechnologyChennaiIndia
  2. 2.School of Mechanical EngineeringVellore Institute of Technology, VITVelloreIndia

Personalised recommendations