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Yuhan Wang - One of the best experts on this subject based on the ideXlab platform.

  • Vision based in-process inspection for countersink in automated drilling and riveting
    Precision Engineering, 2019
    Co-Authors: Fan Yunfei, Nuodi Huang, Yuhan Wang
    Abstract:

    Abstract Countersinks have been widely used for flush rivets of aircraft panels. The quality of countersinking significantly impacts the performance of riveted joints. Thus, a reliable countersink inspection method is typically required in automated aircraft assembly. In current practice, the in-process inspection of countersinks, especially the concurrent inspection of their normal deviation and depth, has not been reported. This paper presents an in-process countersink inspection approach based on machine vision in automated drilling and riveting systems. In this study, the inspection system is firstly developed. With a telecentric lens, the system generates an Orthographic View of the subject being observed to avoid the scaling effect. Moreover, the system is mechanically coupled with the drill unit on the shuttle; consequently, the countersink image is obtained accurately and rapidly. Thereafter, an improved edge-following method is proposed to extract the countersink contour. As the contour is the combination of a circle and an ellipse in a countersink image, the RANSAC algorithm is employed to fit them together. Finally, by Orthographic projection, the countersink imaging process model is established; furthermore, the normal deviation and depth of countersink are derived. The results of a series of experiments demonstrate that the proposed method performs accurately and robustly.

Nam Kim - One of the best experts on this subject based on the ideXlab platform.

Fan Yunfei - One of the best experts on this subject based on the ideXlab platform.

  • Vision based in-process inspection for countersink in automated drilling and riveting
    Precision Engineering, 2019
    Co-Authors: Fan Yunfei, Nuodi Huang, Yuhan Wang
    Abstract:

    Abstract Countersinks have been widely used for flush rivets of aircraft panels. The quality of countersinking significantly impacts the performance of riveted joints. Thus, a reliable countersink inspection method is typically required in automated aircraft assembly. In current practice, the in-process inspection of countersinks, especially the concurrent inspection of their normal deviation and depth, has not been reported. This paper presents an in-process countersink inspection approach based on machine vision in automated drilling and riveting systems. In this study, the inspection system is firstly developed. With a telecentric lens, the system generates an Orthographic View of the subject being observed to avoid the scaling effect. Moreover, the system is mechanically coupled with the drill unit on the shuttle; consequently, the countersink image is obtained accurately and rapidly. Thereafter, an improved edge-following method is proposed to extract the countersink contour. As the contour is the combination of a circle and an ellipse in a countersink image, the RANSAC algorithm is employed to fit them together. Finally, by Orthographic projection, the countersink imaging process model is established; furthermore, the normal deviation and depth of countersink are derived. The results of a series of experiments demonstrate that the proposed method performs accurately and robustly.

Kichul Kwon - One of the best experts on this subject based on the ideXlab platform.

Lingyu Ai - One of the best experts on this subject based on the ideXlab platform.

  • computational integral imaging reconstruction of perspective and Orthographic View images by common patches analysis
    Optics Express, 2017
    Co-Authors: Xiaoyu Jiang, Lingyu Ai
    Abstract:

    A novel method to computationally reconstruct perspective and Orthographic View images with full resolution of a recording device from a single integral photograph is proposed. Firstly, a group of image slices that contain full yet redundant information to reconstruct the View image are generated, and the object surface is divided into pieces by the points that correspond to the centers of image slices. Secondly, the image slices that contribute to the pieces are extracted and redundant information embedded in them are figured out by common patches analysis. Finally, the View image is reconstructed by excluding the redundant information and resampling with maximum sampling rate. Each piece of the object surface is represented with 9 patches at most from 4 adjacent elemental images, and View images with high quality are reconstructed. Both simulations and experiments verify the validity of the method.

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