Automatic lockbolt detection method for riveting quality evaluation based on specific geometric feature descriptors of 3D point cloud

Abstract Lockbolt is increasingly utilized in different fields due to its good performance in riveting and locking. However, the digital measurement and evaluation methods for the riveting quality of lockbolts still need to be explored. In this paper, we proposed a point cloud-based automatic lockbolt detection method, estimating the riveting quality via the protruding height of the lockbolt pin. The method involves three main stages: density computation for 3D point cloud, identification of real lockbolt regions, and segmentation for the top surface of the lockbolt. Firstly, a noise-weakened density computation model is proposed to suppress the density value of noise points. Secondly, the idea of principal component analysis is adopted, and a set of specific geometric feature descriptors is built to identify the real lockbolts from the candidate regions. Lockbolts can be robustly represented even if noise and missing in the point cloud. Thirdly, an efficient section-by-section segmenting algorithm is proposed to segment the top surface of the pin protrusion of the riveted lockbolt. Finally, the riveting quality of each lockbolt is evaluated based on the height from the top surface of the pin protrusion to the bottom plane of the lockbolt. Experiments on real data verified the accuracy, robustness, and efficiency of the proposed method.