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Study on the Visual Detection Method and Experiment Table for Dashboard Framework of a Car

Author: HeDaPeng
Tutor: CuiAn
School: Jilin University
Course: Mechanical Engineering
Keywords: Dashboard skeleton Visual inspection Bench Camera Calibration Image processing
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 40
Quote: 3
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Abstract


The car product competitiveness largely depends on its quality, and the body is an important part of the sedan, its weight and cost accounting for between 40% to 60% of the entire car. The large number of cases showed that the body of most quality problems rooted in assembly manufacturing stage, the direct impact on the overall quality of the body assembly dimensional accuracy. The dashboard skeleton in car body assembly, assembly relation with a body frame and a variety of instruments, meters. Because it is a complex structure of welded parts in the production will inevitably caused by manufacturing deviations, if the failure of the product delivered to the general automobile assembly lines, will not only affect the accuracy of the assembly of the various instruments, meters, and even the deviation is too large can not be completed with the assembly of the body frame, so to pass to the next step before the strict control the quality of the skeleton of the dashboard. Computer vision detection method has a fast, high-precision, flexible, non-contact, etc. are widely used in industrial production and testing. BORA cars dashboard skeleton three defect patterns often appear at the production site (support frame leakage welding, the supporting frames relative position deviation aperture size deviation) visual inspection program, and the key technology in the visual inspection method depth studies, the establishment of a skeleton visual inspection system for BORA car dashboard, validated through experiments on visual inspection system. The design of the detection scheme uses three sets of binocular stereo vision detection system to detect 8 support bracket welded on the skeleton of the dashboard, the \Existence of judgment, the support frame relative positional deviation through the mounting holes to heart detected relative position and the ideal relative position of the comparison to determine the deviation of the radius dimension to determine whether the detected value of the pore diameter of the mounting hole to meet the tolerance requirements. Develop detection scheme, set up a test-bed for the visual inspection of the detection scheme, and the development of a column-car dashboard skeleton visual inspection positioning device provides automated positioning equipment for the the subsequent production site detection. Camera calibration, the vertical movement of the checkerboard as a target to establish the three adaptive neural network to calibrate the two cameras last error analysis obtained comprehensive calibration error: 0.1577mm. In the design of the edge extraction method, this article first image to the background image noise reduction, edge enhancement series pretreatment before applying the canny operator edge extraction. Stereo matching by feature matching, epipolar constraint, regional match, the only constraint on the two image edge of the hole to be matched stepwise selection and the eventual establishment of a one-to-one relationship. Strike method of the coordinates of the center of the hole for the first two edge points of the image of discrete to ellipse fitting obtained respective elliptical center point and then with the neural network to compute the three-dimensional coordinates of the center of the hole. Pore ??radius strike first neural network to obtain the three-dimensional coordinates of the edge point of each of the matching holes, the average distance between the hole center location of these points on the step determined as the detected value of the pore radius, and finally through the error analysis obtained by the visual inspection system for the detection accuracy of less than 0.1mm, meet dashboard skeleton detection accuracy requirements. This study significance is that if this visual detection methods developed in this paper, automatic positioning device applied to the the BORA sedan dashboard skeleton in the actual testing of the production site, not only can improve the detection accuracy and speed, but also through a 100% online testing makes The quality of the skeleton of the dashboard has been effectively controlled.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Information processing (information processing) > Pattern Recognition and devices > Image recognition device
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