With the development of ultra-precision equipment, the roughness of contact surface can reach the nanometer level, and the surface morphology has a significant impact on surface contact, friction, wear and lubrication. At present, the surface morphology description is mainly based on the measurement, which is scale dependent, and the statistical parameters obtained by different sampling length and measurement resolution are different, so it is impossible to realize the accurate characterization of nanoscale rough surface. Because the rough surface is self-affine, another method can be introduced to characterize the rough morphology, fractal theory. The simulated rough surface has the advantage that it is not limited by the sampling length, and can realize the unique characterization of the rough surface. In this study, a nanoscale anisotropic three-dimensional fractal surface is established based on W-M model, and the relationships between fractal dimension D, roughness coefficient G, contour arithmetic mean deviation Sa and contour height standard deviation Sq are studied based on statistical principle. Finally, it is determined that the key parameter for the characterization of nanoscale rough morphology is the roughness coefficient G
The laser interferometric particle imaging technology is used to irradiate the particle spray field with flaky laser beams and collect the scattered light images of the particles on the defocus surface of the imaging lens with a high-speed CCD camera. Based on the existing laser interference particle imaging measurement methods, this paper proposes an automatic recognition algorithm of interference fringe pattern based on morphology Hough transform and a fringe frequency extraction algorithm based on Fourier transform. The overlapping particles are accurately recognized through the improved particle interference fringe pattern recognition algorithm, so as to improve the particle measurement accuracy. Two algorithms are used to simulate and analyze the basic circle and interference fringes, the correct center coordinates and fringe frequency are obtained, and the interference particle images in particle fields with different concentrations are simulated and verified.
High precision occupies an extremely important position in the field of mechanical processing and laser measurement. Under these high precision requirements, image processing is widely applied and is especially demanding. As the essential pre-processing step in the process of image processing, the quality of image edge extraction directly affects the processing precision of the whole image, and then affects the final measurement or machining precision. The traditional edge detection method has the defects of no noise immunity, and cannot achieve high-activity processing of sophisticated image edge problems. Through the in-depth research and analysis of various knowledges concerning edge extraction, a novel anti-noise edge detector based on multi-structure elements morphology of different directions for binary, gray scale and color images is proposed in this paper. We get the final edge information by using eight morphological operations respectively and synthetic weighted method. It can remove the noise effectively while detecting complete edge information. Experimental results show that, comparing with conventional edge detection operations, the proposed method attains the outcome of eliminating the image noise and maintaining good edge effectively for simulated image.
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