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Charaterization of Submerged Arc Welding Seam Formation Based on Binocular Stereo Vision

Author: LuZhongJian
Tutor: ZhangPengXian
School: Lanzhou University of Technology
Course: Materials Processing Engineering
Keywords: Submerged arc weld Weld shape Binocular stereo vision Image processing Forming characterization
CLC: TP391.41
Type: Master's thesis
Year: 2011
Downloads: 25
Quote: 0
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Abstract


Oil long-distance pipeline weld quality welded pipe its demanding, not only to ensure that the welds have good strength and toughness, but also need to have good corrosion resistance. Currently, the main process of a certain thickness to the pipe surface is coated with a preservative to ensure the corrosion resistance of the base material and the weld surface. Actual production weld forming a direct decision of the coating thickness and its preservative effect of surface corrosion layer. Therefore, studying a weld forming automatic detection technology to provide a scientific evaluation methods, not only for the judge weld forming weld post-preservative treatment process also provides the basic data to ensure the preservative effect and reduce preservative Cost significance. Visual images of the surface of the submerged arc weld binocular stereo vision system to obtain information source, explore digitized weld forming judgment. The main contents include: (1) build a set of computer-based binocular stereo vision test system for detecting weld shape. The system can achieve the weld image acquisition, display, storage and image processing, parameter extraction and other features. (2) In order to establish the relationship between the position of the image pixel position with the three-dimensional scene, simplify camera model, respectively, on the visual system calibration and correction. Using Tsai two-step calibration of the camera, get the two cameras within and outside the parameters and the projection matrix. Correction based on the transformation of the projection matrix camera model, it idealized, late greatly simplifies the calculation. (3) through the weld image segmentation techniques, to achieve a characterization of the weld plane information (the length and width of the weld). First, the use of gray-scale normalization and Gaussian filtering methods of weld image preprocessing; Second, the method of selection of secondary dynamic threshold weld split; Finally, the closing operation in morphological processing segment of welding seam zones binary image smoothing and filling treatment. The weld plane finally extracted contour actual consistent, well reflect the weld plane. (4) Based on binocular stereo vision to achieve the the weld height of characterization. For optional grayscale-based matching method, several factors affect the stereo matching: similarity measure function, matching window and similarity threshold for comparative analysis, exploration can get a weld image stereo matching algorithms and parameters. Based on the method of three-dimensional reconstruction of the weld surface, high remainder of each point of the surface of the weld can be visually reflected in the reconstructed image. (5) study the the weld forming parametric characterization methods. First, extract the width of the weld reinforcement and transition angle forming parameters. Secondly, weld formation evaluation: σB (welding wide standard deviation), σc (I high standard deviation), αL (weld left side transition angle), αR (weld transition angle on the right side), ψ (melting aspect ratio), the calculated results with the actual weld forming contrast: the extracted digital evaluation as evaluation of weld formation quantitative indicators. Finally, based on the the weld forming parameters (indicators) and the present general guidelines developed a the weld formation evaluation system table, comprehensive evaluation of weld formation.

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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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