In the evaluation of surface roughness by machine vision technique, the scattered light pattern reflected from the machined surface is generally captured and optical surface finish parameters from the images are correlated with the actual roughness. Capturing of the image at appropriate conditions is required for the good correlation of the optical parameters with the roughness. Lighting conditions is a major factor that influences the image pattern and hence the optical parameters. In this work the lighting conditions like grazing angle, the light to specimen distance, the orientation of the striations on the surface to the light are varied and its influence on the optical surface finish parameter are studied. A plan of experiments based on the techniques of Taguchi was designed and executed for conducting the trials and to obtain valid conclusions. The analysis of variance and the signal to noise ratio of robust design are employed to investigate the influence of different lighting conditions on the optical surface finish parameter. The results of both the approaches confirm that grazing angle is the most influencing factor affecting the image parameter.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Subscribe to journal
Immediate online access to all issues from 2019. Subscription will auto renew annually.
This is the net price. Taxes to be calculated in checkout.
Boothroyd G, Knight WA (1989) Fundamentals of machining and Machine Tools. Dekker, New York
Lee BY, Tarng YS (2001) Surface roughness inspection by computer vision in turning operations. Int J Mach Tools Manuf 41:1251–1263
Gupta M, Raman S (2001) Machine vision assisted characterization of machined surfaces. Int J Prod Res 39(4):759–784
Damodararasamy S, Raman S (1991) Texture analysis using computer vision. Comput Ind 16:25–34
Gadelmawla ES (2004) A vision system for surface roughness characterization using the grey level co-occurrence matrix. NDT&E Int 37:577–588
Kiran MB, Ramamoorthy B, Radhakrishnan V (1998) Evaluation of surface roughness by vision system. Int J Mach tools Manuf 38:685–690
Luk F, Huynh V, North W (1989) Measurement of surface roughness by a machine vision system. J Phys E 22:977–980
Ghani JA, Choudhury IA, Hassan H (2004) Application of Taguchi method in the optimization of end milling parameters. J Mater Process Technol 145:84–92
Choudhury SK, Bartarya G (2003) Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. Int J Mach Tools Manuf 43:747–753
Paulo Davim J (2001) A note on the determination of optimal cutting conditions for surface finish obtained in turning using design of experiments. J Mater Process Technol 116:305–308
Park SH (1996) Robust design and analysis for quality engineering. Chapman & Hall, London
Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2002) Roughness parameters. J Mater Process Technol 123:133–145
Younis MA (1998) Online surface roughness measurements using Image processing towards adaptive control. Comput Ind Eng 35(1–2):49–52
Kumar R, Kulashekar P, Dhanasekar B, Ramamoorthy B (2005) Application of digital image magnification for surface evaluation using machine vision. Int J Mach Tools Manuf 45(2):228–234
Ho S-J, Lee K-C, Chen S-S, Ho S-J (2002) Accurate modeling and prediction of surface roughness by computer vision in turning operations using an adaptive neuro-fuzzy inference system. Int J Mach Tools Manuf 42:1441–1446
Lee K-C, Ho S-J, Ho S-J (2005) Accurate estimation of surface roughness from texture features of the surface image using an adaptive neuro-fuzzy inference system. Precis Eng 29(1):95–100
Priya P, Ramamoorthy B (in press) The influence of component inclination on surface finish evaluation using digital image processing. Int J Mach Tools Manuf
Gadelmawla ES, Koura MM, Maksoud TMA, Elewa IM, Soliman HH (2001) Using the grey level histogram to distinguish between roughness of surfaces. Proc Instn Mech Eng 215B:545–564
Benardos PG, Vosniakos GC (2002) Prediction of surface roughness in CNC face milling using neural networks and Taguchi’s design of experiments. Rob CIM 18:343–354
Phadke MS (1998) Quality engineering using design of Experiments, Quality control, robust design and the Taguchi method. Wadsworth, Los Angeles, CA
Ross P (1998) Taguchi techniques for quality engineering. McGraw-Hill, New York
About this article
Cite this article
Elango, V., Karunamoorthy, L. 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 (2008). https://doi.org/10.1007/s00170-007-0942-y
- Design of experiments
- Machine vision
- Signal to noise ratio
- Surface roughness