Characterization of surface roughness by double blanket model from laser speckle images

Abstract

The surface laser speckle image is obtained by the reflected and scattered light beams from a rough surface illuminated by laser. Based on the fractal theory, Double Blanket Model (DBM) is proposed to analyze laser speckle images. The dimension of the space surface is regarded as the characteristic parameter in DBM method. Laser speckle images are preprocessed to remove interference and noise from the environment at first. The size and direction of optimum window are researched. The DBM characteristic parameter is calculated under the optimum window. The relationships are researched between DBM characteristic parameter and surface roughness Ra. The results show that the surface roughness contained in the surface speckle images has a good monotonic relationship with DBM characteristic parameter. To obtain roughness value through a laser speckle image, the fitting function relationship between Ra and DBM characteristic parameter is established, and the fitting function stability is analyzed by experiments. The experiment results show that surface roughness measurement based on DBM method of laser speckle is feasible and applicable to on-line high-precision roughness detection, which has some advantages such as non-contact, high accuracy, fast, remote measurement and simple equipment.