Tiny hole inspection of aircraft engine nacelle in 3D point cloud via robust statistical fitting. (15th June 2022) - Tang, Hao; Zhou, Laishui; Liu, Yuanpeng; Wang, Jun
Abstract: In the aircraft manufacturing industry, drilling hole inspection is a vital task for the noise reduction ability and the aircraft structure stability. Due to the complexity of the inner surface of the engine nacelle, inspecting drilling holes of a small size and a large number is fairly challenging. In this paper, we propose a framework for automatic hole inspection on composite flat parts. The raw data of traditional 3D laser scanning usually contain considerable noise and outliers, which has a great influence on tiny hole inspection. First, to perform measurement efficiently and to get raw data of good quality, we design a measurement platform to perform automatic measurement on a preset path. Instead of applying common boundary detection methods, we design a method to detect boundary points by evaluating the boundary possibility of each point, which is more likely to detect the points on a real hole boundary than noise. Based on the characteristics of laser scanning data, we then combine an effective algebraic circle fitting method with an iterative fitting strategy and develop a robust circle fitting method. The improved circle fitting method is capable of estimating a circle closest to the ground truth. Experiments demonstrate that our fitting method performs better than other common fitting methods for comparison under both synthetic and real scanning data. Highlights: An automatic inspection framework for tiny holes of composite material is proposed. A measurement platform is designed to collect 3D point clouds of tiny holes. A probability guided method is designed to detect boundary points on hole edge. An iterative circle fitting algorithm for error reduction is proposed.