Fractal-based texture analysis on the SEM images of machined surfaces

Based on the fractal theory, this paper uses the SEM images to investigate the texture characters of the machined surface directly by the surface's own three-dimensional information. For the first time, the Discrete Fractal Brownian Random Field (DFBRF) model is introduced to simulate the surface image, thus maps the grayscale space of the surface image into the surface's fractal parameter space. This paper discusses the relation between the fractal parameters of the surface image and the characters of the surface texture. The DFBRF parameters H, b and sigma are used to describe the roughness and waviness of the machined surface. It shows that the images of the fractal parameter space can efficiently highlight and magnify both the texture features and the directivity of the grayscale surface image, and are proved to be effective for both isotropic and anisotropic surfaces. Consequently it solves the problem of the surface profile's inability in characterizing the anisotropic surface. A group of fractal. parameters are comprehensively considered to detect and recognize the typical singular textures of the surface, and are also used to sort the surfaces into four classes. This method is proved to be effective as expected, and thus find a new way for the fractal analysis of the machined surface.