Study On Grinding Wheel Profile Measurement System Based On Machine Vision

CNC grinding is an effective method to improve machining accuracy and surface quality for the complex surface, but the profile error of grinding wheel will greatly influence machining accuracy, so surface dressing of grinding wheel has been a bottleneck problem in the field. Ordinary grinding wheel is easy to dress, but it wears quickly. It is necessary to frequently dress the grinding wheel, which reduces the grinding efficiency. The grinding wheel with high hardness abrasive, such as CBN grinding wheel, does not need frequent modification, but the modification is difficult for this kind of grinding wheel. Surface envelope grinding method that grinding trajectory is calculated with actual profile is one of effective method to solve the problem. The premise of the method is the prompt measurement for the grinding wheel profile. Therefore, the grinding wheel profile templet measurement method based on machine vision is put forward and the related key technologies are researched in the paper.Machine vision measurement system involves an integration of key techniques such as optical, sensor, image processing, pattern recognition, and so no. It realizes the rapid measurement of object size or relative position. But because of the complexity of the system, there are many random noises and system errors in the measuring process. So far it is a difficult problem to achieve micron level measurement in a larger field of vision. This paper, taking the wheel profile reflection model as a medium to realize the high precision measurement of the wheel profile, through the analysis and research on its detection theory and key technology, puts forward a complete set of system design to make the measurement accuracy of the vision measurement system to±5μmand to meet the requirement of grinding wheel profile measuring. The main contributions of the paper are listed as following.(1)Grinding wheel profile measuring system is built based on machine vision measurement technology, which establishes the hardware foundation for the high precision measurement of wheel profile model. The system not only realizes the precise measurement of wheel profile, but also can replace CMM to realize precision measurement of all kinds of small size plate parts with complex two-dimensional curved contour.(2)According to the structural characteristics of the edge, the anisotropic bilateral filtering algorithm (ABF) is adopted, which can meet the filtering requirements of the image smoothing and edge preserving at the same time. In the spatial domain, the anisotropic Gauss nuclear function is used to redefine the bilateral filter weight, filtering with large scale in the tangential direction in order that the noises are reduced as much as possible and filtering with small scale in the normal direction in order to maintain the edge gradient as far as possible. In the range domain, the gray Gauss nuclear function is used to further reduce the effect of filtering on edge gradient. It solves the problem that the common filter can’t take into account the image smoothing and edge preserving at the same time.(3)Based on Facet surface model subpixel edge detection algorithm, a three-level-approximation subpixel edge detection algorithm is proposed and realized. This method is to use the improved Canny algorithm for coarse positioning to extract the single-pixel-precise edges, and then use the5′5neighborhood data of the pixel edge points to fit Facet surface model and calculate subpixel location in the pixel edge based on the characteristics of the step edge, and finally piecewise fitting edge points as curves to remove the effect of incomplete filtering and calculation error on edge extraction. As a result, the problem of low efficiency, low localization and abnormal fluctuation of edge points for the Facet model to extract edge is fundamentally solved.(4)Because there are various system errors in machine vision system, a system comprehensive calibration method based on straight line imaging characteristics is proposed to effectively ensure the measurement accuracy. According to the extraction of parallel straight line edges of1grade gauge blocks, the original correspondence between the space point positions and the image point positions of the edges is built. Using the ideal linear feature of gauge block edges, comprehensive calibration of measurement system is done through statistical calculation, and a two-parameter second-order polynomial which describes the corresponding relation between the space point positions and the image point positions is built. Because of using the general engineering gauge block as calibration tool, the calibration method is simple to operate and is easy to realize and has good versatility. It can comprehensively correct all kinds of system errors such as the optical distortion, the perspective error, the sensor position error and location error of edge detection algorithm. Based on the calibration method, multiple measurement experiments of different azimuth gauge blocks in the field show that the precision of the measurement system can reach±5μm.