An optical method and neural network for surface roughness measurement

The measurement of surface roughness using the stylus equipment has several disadvantages. A non-contact optical method becomes demanding for measuring the surface roughness of the engineering metals with improved accuracy. One of the candidates for the optical method is the use of a laser source in which case the reflected laser light intensity from the surface may represent the surface roughness of the illuminated area. Consequently, a relation can be developed between the reflected laser beam intensity and the surface roughness of the metals. 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 approximating the peak of the reflected intensity is measured with a fast response photodetector. To achieve this, an experimental setup is designed and realized. In the experimental apparatus, fiber-optic cables are used to collect the reflected beam from the surface. The output of the fiber-optic system is fed to a backpropagation neural network to classify the resulting surface profile and predict the surface roughness value. The results obtained from the present study is, then, compared with the stylus measurement results. It is found that the resolution of the surface texture improves considerably in the case of optical method and the neural network developed for this purpose can classify the surface texture according to the control charts developed mathematically.