24 October 2017 Method of surface error visualization using laser 3D projection technology
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Proceedings Volume 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing; 1045804 (2017) https://doi.org/10.1117/12.2281178
Event: Applied Optics and Photonics China (AOPC2017), 2017, Beijing, China
Abstract
In the process of manufacturing large components, such as aerospace, automobile and shipping industry, some important mold or stamped metal plate requires precise forming on the surface, which usually needs to be verified, if necessary, the surface needs to be corrected and reprocessed. In order to make the correction of the machined surface more convenient, this paper proposes a method based on Laser 3D projection system, this method uses the contour form of terrain contour, directly showing the deviation between the actually measured data and the theoretical mathematical model (CAD) on the measured surface. First, measure the machined surface to get the point cloud data and the formation of triangular mesh; secondly, through coordinate transformation, unify the point cloud data to the theoretical model and calculate the three-dimensional deviation, according to the sign (positive or negative) and size of the deviation, use the color deviation band to denote the deviation of three-dimensional; then, use three-dimensional contour lines to draw and represent every coordinates deviation band, creating the projection files; finally, import the projection files into the laser projector, and make the contour line projected to the processed file with 1:1 in the form of a laser beam, compare the Full-color 3D deviation map with the projection graph, then, locate and make quantitative correction to meet the processing precision requirements. It can display the trend of the machined surface deviation clearly.
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Lili Guo, Lili Guo, Lijuan Li, Lijuan Li, Xuezhu Lin, Xuezhu Lin, } "Method of surface error visualization using laser 3D projection technology", Proc. SPIE 10458, AOPC 2017: 3D Measurement Technology for Intelligent Manufacturing, 1045804 (24 October 2017); doi: 10.1117/12.2281178; https://doi.org/10.1117/12.2281178
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