The efficient extraction and precise measurement of skin seam features are crucial for surface morphology control and optimization in the manufacturing of next-generation aircraft. Visual measurement methods are efficient but cannot extract complete seam features for subsequent surface morphology analysis. Methods based on 3D point cloud have extremely low feature extraction efficiency and cannot quickly extract seam features from large amounts of redundant data. Therefore, this paper combines the advantages of both methods and proposes an extraction and measurement method for aircraft skin seam based on the correlation mapping between point cloud and image. First, the skin scanning point cloud is converted into a 2D image through point cloud projection, grid division of the projection plane, and pixel value determination. Second, an improved U-Net network is used to detect seam regions in the image, and then the corresponding skin data points for the seam region pixels are found based on the mapping relationship equation between the pixels and the skin projection points, so as to obtain the seam feature point cloud. Finally, measurement points are selected in the seam point cloud based on the seam shape, and the flush and gap for each measurement point are calculated. The validation shows that this method can quickly and accurately extract different types of seam features and achieve comprehensive and precise parameter measurement of the seam. The expanded uncertainties for the seam flush and gap measurements are 0.05048 and 0.06766 mm, respectively, with repeatability of 0.0059 and 0.00728 mm, both of which meet the requirements for practical engineering applications.
Deng et al. (Wed,) studied this question.