AN OPTICAL METHOD AND NEURAL-NETWORK FOR SURFACE-ROUGHNESS MEASUREMENT

The measurement of surface roughness using stylus equipment has severa l disadvantages. A non-contact optical method is needed for measuring the surface roughness of engineering metals with improved accuracy. On e candidate for an optical method is the use of a laser source, where the laser light intensity reflected from the surface represents the su rface roughness of the illuminated area. A relation can be developed b etween the reflected laser beam intensity and the surface roughness of the metal. The present study examines the measurement of the surface roughness of the stainless steel samples using a He-Ne laser beam. In the measurement a Gaussian curve parameter of a Gaussian function appr oximating the peak of the reflected intensity is measured with a fast response photodetector.: In order to achieve this, an experimental set up is designed and built. In the experimental apparatus, fiber-optic c ables are used to collect the reflected beam front the surface. The ou tput of the fiber-optic system is fed to a back-propagation neural net work to classify the resulting surface profile and predict the surface roughness value. The results obtained from the present study are then compared with the stylus measurement results. It is found that the re solution of the surface texture improves considerably in the case of o ptical method and the neural network developed for this purpose can cl assify the surface texture according to the control charts developed m athematically. (C) 1997 Published by Elsevier Science Ltd.