A novel technique for calibrating crucial parameters of chassis components is proposed, which utilizes the machine vision metrology to measure 3D coordinates of the center of a component's hole for assembling in the 3D world coordinate system. In the measurement, encoding marks with special patterns will be assembled on the chassis component associated with cross drone and staff gauge located near the chassis. The geometry and coordinates of the cross drone consist of two planes orthogonal to each other and the staff gauge is in 3D space with high precision. A few images are taken by a highresolution camera in different orientations and perspectives. The 3D coordinates of 5 key points on the encoding marks will be calculated by the machine vision technique and those of the center of the holes to be calibrated will be calculated by the deduced algorithm in this paper. Experimental results show that the algorithm and the technique can satisfy the precision requirement when the components are assembled, and the average measurement precision provided by the algorithm is 0.0174 mm.The precise calibration of the chassis is crucial to the performance of motorcycles, electric bicycles and bicycles , especially for the stability and safety of these vehicles, which fully depend on the precision of the junction between the chassis and other components. In order to accurately assemble the components, the precise calibration of the key points of the chassis components is indispensable and significant [1] . The 3D calibration machine is widely used to measure the parameters of the chassis, which needs to touch the components [2] . The component to be calibrated must be fixed on the machine and measured point by point [3] . It will cost half an hour per time. Due to the waste of time and the inconvenience, lots of manufactories can not have enough energy and resource to ensure every chassis to be calibrated, resulting in just only a part of chassis can be measured.The novel technique proposed in this paper is focused on the machine vision calibration. In the technique, an encoding mark with a special pattern serves as the medium to obtain the 3D coordinates of the center of the component's hole for assembling [4][5][6] . The 3D coordinates of 5 key points on the encoding marks can be obtained by the high-precision machine vision technique [7][8][9][10] . Each calibration takes about 3 min and the 0.0174 mm measurement accuracy means that it can meet the necessary calibration and assembly requirement.With the technique and algorithm, the chassis can be fully or 80% calibrated, which contributes to promote the product quality of chassis components. When the unqualified product is detected in the manufacture process of chassis, the immediate repairment can be offered or the subsequent processing can be ceased in order to avoid dispensable waste.Since the hollow nature of the hole, the coordinates of the center of the assembled hole can , t be directly calibrated. The traditional method can only provide fitting coordinates for the ...
The fabric quality defect detection is very useful for improving the qualities of the products. It is also very important to increase the reputation and the economic benefits of a company. However, there are some shortcomings in the traditional manual detection methods, such as the low detection efficiency, the fatigue problem of the operator, and the detection inaccuracy, etc. The existing 2D image processing methods are difficult to solve the interference which is caused by non-defect case, just like the cloth folds, the flying thick silk floss, the noise from the background light and ambient light, etc. In order to solve those problem, the BCCSL (Binocular Camera Color Structure Light) method and SFMS (Shape from Multi Shading) method is proposed in this paper. The three-dimensional color coordinates of the fabric can be quickly and highly-precision obtained, thus to judge the defects shape and location.The BCCSL method and SFMS method can quickly obtain the three-dimensional coordinates' information of the fabric defects. The BCCSL method collects the 3D skeleton's information of a fabric image through the binocular video capture device and the color structured light projection device in real-time. And the details 3D coordinates of fabric outside strip structural are obtained through the proposed method SFMS. The interference information, such as the cloth fold, the flying thick silk floss, and the noise from the background light and ambient light can be excluded by using the three-dimensional defect identification. What is more, according to the characteristics of 3D structure of the defect, the fabric can be identified and classified. Further more, the possible problems from the production line can be summarized.
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