Measurement of surface roughness of metals using binary speckle image analysis

In this paper, results from an optical technique for measuring surface roughness using image analysis of speckle pattern images are presented. The technique coined as statistical properties of binary images (SPBI) utilizes the combined effects of speckle and scattering phenomena. The speckle patterns obtained with a He–Ne laser were binarized and examined. The parameters such as bright and dark regions and their ratios obtained from this model to evaluate the surface roughness were compared with the surface roughness parameter R_a obtained from a profilometer. It was found that there is a strong relationship between these parameters and R_a, especially in the range of λ<R_a<2λ where λ is He–Ne laser wavelength. Although, it is a relative method, it has great potential to be used for in-process measurement and automation due to the simplicity of optical system used. The proposed method for the surface roughness combined with a non-contact optical measuring system is applied to samples from 0.5825 to 1.9 μm of steel (CK 45) through CNC face-milling process.