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Erschienen in: The International Journal of Advanced Manufacturing Technology 1-2/2020

20.02.2020 | ORIGINAL ARTICLE

Point cloud 3D parent surface reconstruction and weld seam feature extraction for robotic grinding path planning

verfasst von: Xiangfei Wang, Xiaoqiang Zhang, Xukai Ren, Lufeng Li, Hengjian Feng, Yanbing He, Huabin Chen, Xiaoqi Chen

Erschienen in: The International Journal of Advanced Manufacturing Technology | Ausgabe 1-2/2020

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Abstract

High-performance components with complex geometries make it difficult to determine the position and orientation of grinding tool. In this work, a fast and accurate robotic grinding path planning method is proposed for automatic removal of irregular weldments on a free form surface. The surface of workpiece is digitalized by 3D profile scanner and represented by point cloud data. Statistic filter, weighted least square regression and differences of normal vectors are used for point cloud pre-processing and segmentation. All segments are then modelled by B-spline surfaces to obtain the parent surface. A new superposition method is proposed to create a computer-aided design (CAD) model of the actual workpiece by adding the weld seam to the parent surface. The CAD model is then imported into an off-line simulation system to generate and execute grinding path. With the superposition method, the heights and widths of weld seam are extracted by analysing the difference between point cloud data and the reconstructed parent surface in order to determine the feed rate and size of grinding tool. Experimental results show that the proposed superposition method has the maximum absolute percentage error 5.3% and 41% saving in computation time in comparison with the conventional reverse engineering method.

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Metadaten
Titel
Point cloud 3D parent surface reconstruction and weld seam feature extraction for robotic grinding path planning
verfasst von
Xiangfei Wang
Xiaoqiang Zhang
Xukai Ren
Lufeng Li
Hengjian Feng
Yanbing He
Huabin Chen
Xiaoqi Chen
Publikationsdatum
20.02.2020
Verlag
Springer London
Erschienen in
The International Journal of Advanced Manufacturing Technology / Ausgabe 1-2/2020
Print ISSN: 0268-3768
Elektronische ISSN: 1433-3015
DOI
https://doi.org/10.1007/s00170-020-04947-1

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