Noncontact Surface Roughness Assessment Using Machine Vision System

  • Dhiren PatelEmail author
  • Kiran Mysore
  • Kartikkumar Thakkar
Conference paper
Part of the Lecture Notes in Mechanical Engineering book series (LNME)


The use of the optical device for the measurement of surface roughness reduces complexity and time for measurement. In the current study, surface roughness parameters were measured after machining on a shaper machine using the machine vision system which was compared with that obtained through the stylus method. Machining operation involves complexity and produces different surface finish with different cutting conditions, therefore in the present study correlation between surface roughness parameters (viz. arithmetic average height (Ra); maximum height of peaks (Rp); root mean square roughness (Rq); maximum height of the profile (Rt), and ten-point height (Rz)) and optical surface finish parameters (i.e., mean, standard deviation, skewness and kurtosis) has been developed for varied values of cutting parameters (i.e., depth of cut and RPM of pulley drive). The linear relation model with optical parameters and surface roughness parameters has been developed. It was observed that all the roughness parameters can be estimated with a fair degree of accuracy (R2 > 0.92) using optical statistical parameter kurtosis, while means, skewness, and standard deviation obtained through the same image processing data fail to estimate roughness parameters.


Machine vision system Roughness parameters Surface profilometer Kurtosis 


  1. 1.
    Asiltürk I, Çunkaş M (2011) Modeling and prediction of surface roughness in turning operations using artificial neural network and multiple regression method. Expert Syst Appl 38(5):5826–5832CrossRefGoogle Scholar
  2. 2.
    Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2002) Roughness parameters. J Mater Process Technol 123(1):133–145CrossRefGoogle Scholar
  3. 3.
    Singh SK, Srinivasan K, Chakraborty D (2004) Acoustic characterization and prediction of surface roughness. J Mater Process Technol 152(2):127–130CrossRefGoogle Scholar
  4. 4.
    Poon CY, Bhushan B (1995) Comparison of surface roughness measurements by stylus profiler, AFM and non-contact optical profiler. Wear 190(1):76–88CrossRefGoogle Scholar
  5. 5.
    Ghodrati S, Mohseni M, Kandi SG (2015) Dependence of adhesion strength of an acrylic clear coat on fractal dimension of abrasive blasted surfaces using image processing. In: 6th international color and coating congressGoogle Scholar
  6. 6.
    Radhakrishnan V (1970) Effect of stylus radius on the roughness values measured with tracing stylus instruments. Wear 16(5):325–335CrossRefGoogle Scholar
  7. 7.
    Sherrington I, Smith EH (1988) Modern measurement techniques in surface metrology: part I; stylus instruments, electron microscopy and non-optical comparators. Wear 125(1):271–288CrossRefGoogle Scholar
  8. 8.
    Chen S, Feng R, Zhang C, Zhang Y (2018) Surface roughness measurement method based on multi-parameter modeling learning. Measurement 129(12):664–676CrossRefGoogle Scholar
  9. 9.
    Pfeifer T, Wiegers L (2000) Reliable tool wear monitoring by optimized image and illumination control in machine vision. Measurement 28(3):209–218CrossRefGoogle Scholar
  10. 10.
    Priya P, Ramamoorthy B (2007) The influence of component inclination on surface finish evaluation using digital image processing. Int J Mach Tools Manuf 47(3–4):570–579CrossRefGoogle Scholar
  11. 11.
    Al-Kindi GA, Shirinzadeh B (2009) Feasibility assessment of vision-based surface roughness parameters acquisition for different types of machined specimens. Image Vis Comput 27(4):444–458CrossRefGoogle Scholar
  12. 12.
    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(1–2):92–103CrossRefGoogle Scholar
  13. 13.
    Younis MA (1998) On line surface roughness measurements using image processing towards an adaptive control. Comput Ind Eng 35(1–2):49–52CrossRefGoogle Scholar
  14. 14.
    Gadelmawla ES (2011) Estimation of surface roughness for turning operations using image texture features. Proc Inst Mech Eng Part B J Eng Manuf 225(8):1281–1292CrossRefGoogle Scholar
  15. 15.
    Tsai D, Chen J, Chert J (1998) A vision system for surface roughness assessment using neural networks. Int J Adv Manuf Technol 14(6):412–422CrossRefGoogle Scholar
  16. 16.
    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 Circuits, Syst Comput 21(1):1–14CrossRefGoogle Scholar
  17. 17.
    Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tools Manuf 41(9):1251–1263CrossRefGoogle Scholar
  18. 18.
    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(4):295–301CrossRefGoogle Scholar
  19. 19.
    Kumar R, Kulashekar P, Dhanasekar B, Ramamoorthy B (2005) Application of digital image magnification for surface roughness evaluation using machine vision. Int J Mach Tools Manuf 45(2):228–234CrossRefGoogle Scholar
  20. 20.
    Demircioglu P, Bogrekci I, Durakbasa NM (2013) Micro scale surface texture characterization oftechnical structures by computer vision. Measurement 46(6):2022–2028CrossRefGoogle Scholar
  21. 21.
    Jeyapoovan T, Murugan M (2013) Surface roughness classification using image processing. Measurement 46(7):2065–2072CrossRefGoogle Scholar
  22. 22.
    Kostakos J (2013) Relation between surface roughness and adhesion as studied with AFM. Ph.D thesis, University of TwenteGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  • Dhiren Patel
    • 1
    Email author
  • Kiran Mysore
    • 1
  • Kartikkumar Thakkar
    • 1
  1. 1.Pandit Deendayal Petroleum UniversityGandhinagarIndia

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