Abstract
This paper presents a technique for measuring surface roughness (Ra ), using photometric stereo method. The method utilizes three or more images of the measured surface from the same viewpoint, taken sequentially under different illumination conditions. The scenes captured by the camera were reconstructed by the least square method to obtain surface normal from surface topography. Three-dimensional surface textural patterns were recovered from those surface normal. The system was validated with five standard surface roughness specimens. The Ra calculated from the recovered surface was compared with the values measured from contacting roughness measurement (stylus instrument). The best coefficient of the slant angles can also be determined through the linear regression. The experimental results indicate that the technique can be applied to well recover standard surface roughness.
Highlights
Computer graphics techniques have been extensively popular in the measurement system
We found that the optimal slant angle for measuring the roughness standard between 3.2 mm and 50 mm was 45°
The experimental results have shown that the new approach to surface textural measurement using photometric stereo method and Coordinate Measuring Machine (CMM) is capable of measuring surface roughness of work pieces
Summary
Computer graphics techniques have been extensively popular in the measurement system. The traditional way of measuring surface topography is mechanical – most existing roughness standards are defined using stylus instruments that normally use a diamond stylus. In the last few decades, alternative methods rather than stylus have been developed. Photometric stereo method has been considered as a technique for roughness measurement. The photometric stereo is a method for evaluation of shape and reflectance of an object using three or more images under different lighting positions [3]. The method uses different lighting conditions to mainly measure the gradient field of the surface, which is calculated from an array of surface normal. We consider the slant and tilt angles which are suitable for measuring different roughness ranging from 3.2 mm to 50 mm. The key point of the proposed method is that the linear regression technique was applied to find the best coefficient between image intensities and roughness
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