| Summary: | 碩士 === 元智大學 === 機械工程學系 === 107 === Welding is the main processing method used to connect metal workpiece in the industrial system. Automatic extraction of weld track is the key of welding automation. In recent years, due to rapid progresses in the hardware and software technology, deep learning technology has been widely used and achieved good results. This paper proposes an automatic welding seam extraction method of weld seam trajectory from point cloud data based on deep learning. By accurately registering the point cloud of the workpiece to its corresponding generated from the CAD model point cloud, the precise location of the weld seam of the CAD model relative to the workpiece can be obtained. Finally, the weld seam trajectory of the workpiece is extracted by using the fast nearest point search method. The research contents are as follows:
(1) Obtain point cloud data. Build the workpiece point cloud acquisition platform, load the laser scanning sensor and other related hardware, use a stepper motor to move the sensor to scan the workpiece, obtain the three-dimensional point cloud data of the workpiece surface, and obtain the CAD model and CAD weld point cloud data by the existing three-dimensional software.
(2) Coarse positioning of weld track of workpiece. In order to improve the registration efficiency, the point cloud and the CAD point cloud of a workpiece are pre-processed to reduce the number of point clouds data points. Then, the point cloud registration algorithm based on deep learning is adopted to register the point cloud of the workpiece and the point cloud of the CAD model. In other words, the initial position of the CAD welding seam relative to the workpiece is pre-adjusted to obtain the approximate position of the welding seam track of the workpiece.
(3) Accurate positioning of the weld track of the workpiece. Due to the large number of redundant points in the workpiece point cloud, a feature point cloud extraction method based on surface curvature is proposed. The improved ICP algorithm is used to accurately register the CAD weld point cloud after the characteristic point cloud of the workpiece and the pre-adjusted position, and obtain the precise gesture of the CAD weld point cloud relative to the workpiece point cloud.
(4) Extraction of weld track of workpiece. Using the fast nearest point search algorithm based on KD-Tree, the nearest point on the workpiece point cloud is obtained from the CAD weld point cloud after adjusting bit posture, which can be used as the workpiece weld track. Calculate the distance between the pre-adjusted CAD weld point cloud and the CAD weld point cloud adjusted to the precise position and the nearest point of the workpiece weld point cloud data extracted by them respectively for error analysis. The error analysis results demonstrate that the extracted data of the workpiece weld point cloud basically meet the industrial requirements. Finally, the efficiency and registration accuracy of the proposed registration algorithm are verified by registration experiments.
Key words：Weld Extraction, Point Cloud Registration, Deep Learning, Feature Extraction
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