Machine Vision for Surface Roughness Assessment of Inclined Components

Article Preview

Abstract:

Many researchers have so far used machine vision and digital image processing for grabbing images of machined surfaces, improving their quality by pre-processing and then analysed them for evaluation of surface finish with a reasonable success. An attempt has been made in this work to capture the images of the surfaces with varying inclinations covering both the sides. The ideal orientation of the surface (flat and horizontal) is found by observing the variation in optical roughness parameters estimated from the grey level co-occurrence matrix as the angle of inclination changes. It is observed that the variation of roughness parameters with respect to angle of inclination also depends on the surface roughness of the component. The optical roughness values obtained by machine vision approach are then subsequently compared with the conventional Ra as obtained by stylus method and the analysis is presented.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

141-144

Citation:

Online since:

May 2010

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2010 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] D.J. Whitehouse: Surface metrology. Measurement science technology, 8 (1997), pp.955-972.

Google Scholar

[2] S. Damodarasamy and S. Raman: Texture analysis using computer vision. Computers in industry, 16 (1991), pp.25-34.

DOI: 10.1016/0166-3615(91)90005-t

Google Scholar

[3] G.A. Al-Kindi, R.M. Baul and K.F. Gill: An application of machined vision in the automated inspection of engineering surfaces. International Journal of Production Research, 30 (1992), No. 2, pp.241-253.

DOI: 10.1080/00207549208942892

Google Scholar

[4] F. Luk, V. Huynh and W. North: Measurement of surface roughness by a machine vision system. Journal of Physics E (Scientific Instruments), 22 (1989), No. 12, pp.977-80.

DOI: 10.1088/0022-3735/22/12/001

Google Scholar

[5] K. Venkat Ramana and B. Ramamoorthy: Statistical methods to compare the texture features of machines surfaces. Pattern recognition, 29 (1996), No. 9, pp.1447-1459.

DOI: 10.1016/0031-3203(96)00008-8

Google Scholar

[6] M.R. Haralick, K. Shanmugam and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, 3 (6) (1973), pp.610-621.

DOI: 10.1109/tsmc.1973.4309314

Google Scholar

[7] E.S. Gadelmawla: A vision system for surface roughness characterization using the gray level co-occurrence matrix. NDT&E, 37 (2004), pp.577-588.

DOI: 10.1016/j.ndteint.2004.03.004

Google Scholar