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Weld-seam identification and model reconstruction of remanufacturing blade based on three-dimensional vision
Affiliation:1. State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing 400044, China;2. Key Laboratory of Optical System Advanced Manufacturing Technology, Chinese Academy of Sciences, Changchun 130000, China;1. Laboratory for Artificial Intelligence in Design, Hong Kong, China;2. Department of Industrial and Systems Engineering, The Hong Kong Polytechnic University, Hung Hom, Kowloon, Hong Kong, China;1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai, PR China;2. Beijing Institute of Electronic System Engineering, Beijing, PR China;3. School of Economics and Management, University of the Chinese Academy of Sciences, Beijing, PR China;4. School of Cyberspace Science and Technology, Beijing Institute of Technology, Beijing, PR China;5. HKU-ZIRI Lab for Physical Internet, Dept. of Industrial and Manufacturing Systems Engineering, The University of Hong Kong, Pokfulam Road, Hong Kong, China
Abstract:Remanufacturing technique has been widely used for repairing the damaged regions of aero-engine blades. As the irregularity of weld-seam shape and uneven residual height pose great challenges for the subsequent precision grinding and polishing, it is necessary to use the high efficiency and precision measurement method to obtain a true and accurate weld-seam model, especially in the boundary areas. Therefore, a measurement approach for weld-seam identification and model reconstruction of the remanufacturing blade based on self-developed binocular vision system is presented in this work. The calculation of three-dimensional reconstruction and the complexity of subsequent point cloud processing were reduced by coarse positioning firstly, and then the weld-seam was located precisely by point cloud segmentation based on the region growth algorithm and point cloud normal filtering method. On this basis, a true weld-seam model with precise boundary was obtained. The average error of the boundary extracted by proposed method is about 0.263 mm lower than that of the traditional method. In addition, the B-spline surface was fitted according to the point cloud without weld-seam feature, and a theoretical machining model was obtained by cutting of B-spline surface along the weld-seam boundary. Furthermore, the results of verification experiment indicated that the smoothness error of machined blade surface was less than 0.025 mm and the machining error was less than 0.07 mm. This method has obvious advantages in calculation efficiency and reconstruction accuracy of theoretical machining model while compared with traditional measurement methods for the remanufacturing blade, which is beneficial to improve the machining quality and efficiency of the remanufacturing parts.
Keywords:Binocular vision  Three-dimensional reconstruction  Point cloud denoising  Normal filtering  Remanufacturing blade  Weld-seam
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