Abstract：To solve the problem of verticality detection of aviation drilling holes on site, a fast and high-precision method is proposed based on laser ranging sensor. Firstly, an expansion mandrel is stably supported in the hole to simulate its axis. Then the point cloud data of the holes end face is obtained with the ranging sensor and an angle encoder, which are both installed on the mandrel. And the normal vector of the face is further fitted. Finally, the angle between the vector and the axis is calculated as the verticality. At the same time, a 3D point cloud reconstruction algorithm based on RANSAC is proposed to improve the measurement accuracy. The systematic error, caused by the reference reflected by the expansion mandrel, is compensated after the vectors are synthesized. The measuring device is calibrated by verticality gauges and the experiments of verticality detection are completed for both plane and curved parts. The result shows that the measurement repeatability is less than 0.01° and the maximum indication error is within ±0.1°. It takes less than one minute for each hole. So the requirements of the accuracy and efficiency for on-site detection can be met.
Xing X L, Gan W B, Jiang C G. Measurement Technology of Aero rivets Based on Machine Vision［J］. Acta Metrologica Sinica, 2020, 41(5): 518-523.
［2］
Bi S, Liang J. Robotic drilling system for titanium structures［J］. The international journal of advanced manufacturing technology, 2011, 54(5): 767-774.
［4］
Shemeta P J. Angularity gage: U. S. 9354032［P］. 2016-05-31.
Bi Y B, Xu C, Fan X T, et al. Measuring Method of Countersunk Hole Verticality Based on Vision Measurement［J］. Journal of Zhejiang University (Engineering Science), 2017, 51(2): 312-318.
［8］
徐超. 基于视觉测量的沉头孔垂直度检测新技术研究［D］. 杭州: 浙江大学, 2016.
［10］
Johnson M L, Faunt L M. Parameter Estimation by Least-squares Methods［J］. Methods in Enzymology, 1992, 210: 1-37.
Chum O, Matas J, Kittler J. Locally optimized RANSAC［C］//Joint Pattern Recognition Symposium. Springer, Berlin, Heidelberg, 2003: 236-243.
［3］
袁红璇. 飞机结构件连接孔制造技术［J］. 航空制造技术, 2007(1): 96-99.
He F T, Chen X M, Xiong Y L, et al. Research Status and Development Trend of Surface Automatic Hole Making Technology［J］. Science Technology and Engineering, 2020, 20(29): 11834-11841.
Xie Z N, Jiang Z H, Zhu H C, et al. Research on Fitting Method of Central Axis of Three-dimensional Point Cloud Thread［J］. Acta Metrologica Sinica, 2020, 41(8): 918-925.
Wang Y, Liang J C. Analysis of Measurement Process Characteristics Based on Measurement Uncertainty［J］. Acta Metrologica Sinica, 2022, 43(9): 1241-1244.
［19］
熊有伦. 机器人技术基础［M］. 武汉: 华中科技大学出版社, 1996: 15-22.
［6］
Yu L, Bi Q, Ji Y, et al. Vision Based in-process Inspection for Countersink in Automated Drilling and Riveting［J］. Precision Engineering, 2019, 58: 35-46.
Shi X L, Du K P, Zhang J W, et al. Parameter Extraction Method for Aircraft Surface Countersink Holes based on Line Laser Scanning［J］. Journal of Mechanical Engineering, 2020, 56(8): 148-154.
Kong D M, Huang Z S, Wang S T, et al. NURBS Surface Fitting Based on Fractional Fourier Transform［J］. Acta Metrology Sinica, 2021, 42(3): 294-300.
［14］
Fischler M A, Bolles R C. Random Sample Consensus: A Paradigm for Model Fitting with Applications to Image Analysis and Automated Cartography［J］. Communications of the ACM, 1981, 24(6): 381-395.
［16］
Raguram R, Chum O, Pollefeys M, et al. USAC: A Universal Framework for Random Sample Consensus［J］. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2012, 35(8): 2022-2038.
2023, Vol. 44 
