To address the issue of large data volume and information redundancy in turbine blade point cloud data acquired through industrial computerized tomography scanning, which leads to difficulties in subsequent data processing, this paper proposes a point cloud simplification method that preserves sharp features. This method segments feature regions based on curvature meshes, calculates curvature feature values, identifies and retains feature points, and conducts a secondary reduction on non-feature points. By merging the feature points with the simplified non-feature points, the method achieves simplification of high-density point cloud data while preserving sharp features. Experiments indicate that this approach can effectively remove redundant points from turbine blades, retain sharp features of the point cloud, and ensure the precision requirements of the point cloud are met.
On-machine measurement plays a key role in adaptive machining technology, providing real-time information on machining parameters and material state. Aiming at the compensation error that will be introduced after the radius compensation of the on-machine probe, this paper suggests a method of blade profile measurement data alignment considering the probe radius by improving the objective function of the nearest-point iteration algorithm based on the mean square deviation of the distance from the corresponding point. The reliability of the method is verified through simulation and real data alignment experiments, which provides an effective way to improve the alignment accuracy.
During the process of scanning the shape of a large object, multiple scans from different angles are required due to the influence of surface complexity and scanning field of view, and then these data are stitched together into a whole. This paper proposes a tracking-based optical measurement point cloud stitching method based on circular coded points. During the scanning of the object by the structured light scanner, the binocular tracking camera real-time locates and pastes the circular coded points on the scanner. Using this marker as an intermediary, the reconstructed 3D point cloud in the coordinate system of the structured light scanner is transformed to the reference coordinate system of the positioning and tracking binocular camera to achieve point cloud stitching and complete the reconstruction of the three-dimensional morphology information of the object being measured.
Extracting the stripe center during the measurement process can significantly improve the measurement accuracy, and the optical stripe center extraction algorithm is an important factor determining the accuracy of the optical stripe contour location and extraction speed. For the traditional optical stripe center extraction method, because the ambient light conditions will affect the stripe center extraction under certain circumstances, the stripe center extraction often cannot accurately obtain the centerline, and to a certain extent, the extraction time of each algorithm is increased. In this paper, a line laser stripe center extraction method based on channel separation is proposed. The measured area is separated from the background area by the image segmentation algorithm, the Hessian matrix is obtained for each part of the measured area, and its normal direction is determined to obtain the sub-pixel stripe center, which can effectively avoid the problems of high light and certain brightness of the object, improve stripe center extraction speed and reduce stripe center noise.
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