A Model of Diameter Measurement Based on the Machine Vision

Symmetry - Tập 13 Số 2 - Trang 187
Qing Tan1, Ying Kou1, Jianwei Miao1, Siyuan Liu1, Bosen Chai1
1Institute of Mechanical and Aerospace Engineering, Jilin University, Changchun 130025, China

Tóm tắt

If the shaft diameter can be measured in-situ during the finishing process, the closed-loop control of the shaft diameter processing process can be realized and the machining accuracy can be improved. Present work studies the measurement of shaft diameter with the structured light system composed of a laser linear light source and a camera. The shaft is a kind of part with rotationally symmetric structure. When the linear structured light irradiates the surface of the shaft, a light stripe will be formed, and the light stripe is a part of the ellipse. Therefore, the in-situ measurement of the shaft diameter can be realized by the light stripe and the rotational symmetry of the shaft. The measurement model of shaft diameter is established by the ellipse formed by the intersection of the light plane and the measured shaft surface. Firstly, in the camera coordinate system, normal vector of the light plane and the coordinates of the ellipse center are obtained by the calibration; then, the equation of oblique elliptic cone is established by taking the ellipse as the bottom and the optical center of the camera as the top. Next, the measurement model of shaft diameter is obtained by the established oblique elliptic cone equation and theoretical image plane equation. Finally, the accuracy of the measurement model of shaft diameter is tested by the checkerboard calibration plate and a lathe. The test results show that the measurement model of shaft diameter is correct, and when the shaft diameter is 36.162mm, the speed is 1250r/min, the maximum average measurement error is 0.019mm. The measurement accuracy meets the engineering requirement.

Từ khóa


Tài liệu tham khảo

Zhang, 2020, Multi-information online detection of coal quality based on machine vision, Powder Technol., 374, 250, 10.1016/j.powtec.2020.07.040

Sun, W., and Yeh, S. (2018). Using the Machine Vision Method to Develop an On-machine Insert Condition Monitoring System for Computer Numerical Control Turning Machine Tools. Materials, 11.

Makela, M., Rissanen, M., and Sixta, H. (2020). Machine vision estimates the polyester content in recyclable waste textiles. Resour. Conserv. Recycl., 161.

Jones, J., Foster, W., Twomey, C., Burdge, J., Ahmed, O., Pereira, T., Wojick, J., Corder, G., Plotkin, J., and Abdus-Saboor, I. (2020). A machine-vision approach for automated pain measurement at millisecond timescales. eLife, 9.

Sun, X., Xu, S., and Lu, H. (2020). Non-Destructive Identification and Estimation of Granulation in Honey Pomelo Using Visible and Near-Infrared Transmittance Spectroscopy Combined with Machine Vision Technology. Appl. Sci., 10.

Viejo, 2018, Robotics and computer vision techniques combined with non-invasive consumer biometrics to assess quality traits from beer foamability using machine learning: A potential for artificial intelligence applications, Food Control, 92, 72, 10.1016/j.foodcont.2018.04.037

Zhang, Z., Wang, X., Zhao, H., Ren, T., Xu, Z., and Luo, Y. (2020). The Machine Vision Measurement Module of the Modularized Flexible Precision Assembly Station for Assembly of Micro- and Meso-Sized Parts. Micromachines, 11.

Li, 2010, Method of rotation angle measurement in machine vision based on calibration pattern with spot array, Appl. Opt., 49, 1001, 10.1364/AO.49.001001

Lian, F., Tan, Q., and Liu, S. (2019). Block Thickness Measurement of Using the Structured Light Vision. Int. J. Pattern Recognit. Artif. Intell., 33.

Chen, J., Jing, L., Hong, T., Liu, H., and Glowacz, A. (2020). Research on a Sliding Detection Method for an Elevator Traction Wheel Based on Machine Vision. Symmetry, 12.

Shen, 2012, Bearing defect inspection based on machine vision, Measurement, 45, 719, 10.1016/j.measurement.2011.12.018

Moru, 2019, A machine vision algorithm for quality control inspection of gears, Int. J. Adv. Manuf. Technol., 106, 105, 10.1007/s00170-019-04426-2

Wu, 2018, Three-line structured light measurement system and its application in ball diameter measurement, Optik, 157, 222, 10.1016/j.ijleo.2017.11.068

Bao, H., Tan, Q., Liu, S., and Miao, J. (2019). Computer Vision Measurement of Pointer Meter Readings Based on Inverse Perspective Mapping. Appl. Sci., 9.

Guo, 2011, Mini milling cutter measurement based on machine vision, Procedia Eng., 15, 1807, 10.1016/j.proeng.2011.08.336

Gao, 2018, Vision measurement technique of axle based on double beam, Optik, 174, 757, 10.1016/j.ijleo.2018.08.131

Wei, 2011, Measurement of shaft diameters by machine vision, Appl. Opt., 50, 3246, 10.1364/AO.50.003246

Sun, 2014, Shaft diameter measurement using a digital image, Opt. Laser Eng., 55, 183, 10.1016/j.optlaseng.2013.11.005

Li, B. (2018). Research on geometric dimension measurement system of shaft parts based on machine vision. EURASIP J. Image Video Process., 10.

Che, 2018, Real-time monitoring of workpiece diameter during turning by vision method, Measurement, 126, 369, 10.1016/j.measurement.2018.05.089

Liu, 2015, Shaft Diameter Measurement Using Structured Light Vision, Sensors, 15, 19750, 10.3390/s150819750

Liu, 2012, An improved online dimensional measurement method of large hot cylindrical forging, Measurement, 45, 2041, 10.1016/j.measurement.2012.05.004

Wu, 2010, A novel method for round steel measurement with a multi-line structured light vision sensor, Meas. Sci. Technol., 21, 025204, 10.1088/0957-0233/21/2/025204

Liu, 2010, Measuring method for micro-diameter based on structured-light vision technology, Chin. Opt. Lett., 8, 666, 10.3788/COL20100807.0666

Gong, Z., Sun, J., and Zhang, G. (2016). Dynamic Measurement for the Diameter of a Train Wheel Based on Structured-Light Vision. Sensors, 16.

Zhang, 2020, Accurate profile measurement method for industrial stereo-vision systems, Sens. Rev., 40, 445, 10.1108/SR-04-2019-0104

Malyarchuk, 2010, Experimental and modeling studies of imaging with curvilinear electronic eye cameras, Opt. Express, 18, 27346, 10.1364/OE.18.027346

Zhang, 2003, A position-distortion model of ellipse centre for perspective projection, Meas. Sci. Technol., 14, 1420, 10.1088/0957-0233/14/8/331

Li, 2010, Frequency-domain streak camera for ultrafast imaging of evolving light-velocity objects, Opt. Lett., 35, 4087, 10.1364/OL.35.004087

Zhang, 2018, A single-image linear calibration method for camera, Measurement, 130, 298, 10.1016/j.measurement.2018.07.085

Lv, B., Li, L., and Yan, C. (2016, January 17–19). Three-dimensional laser scanning under the pinhole camera with lens distortion. Proceedings of the 4th IEEE International Conference on Cloud Computing and Intelligence Systems (IEEE CCIS), Beijing, China.

Zhang, 2000, A flexible new technique for camera calibration, IEEE Trans. Pattern Anal. Mach. Intell., 22, 1330, 10.1109/34.888718

Bouguet, J.Y. (2000). Pyramidal Implementation of the Lucas Kanade Feature Tracker Description of the Algorithm, OpenCV Document. Intel Microprocessor Research Labs. Technical Report.

Bouguet, J.Y. (2020, June 05). Camera Calibration Toolbox for Matlab. Available online: https://www.vision.caltech.edu/bouguetj/calib_doc/index.html.

Markovsky, 2004, Consistent least squares fitting of ellipsoids, Numer. Math., 98, 177, 10.1007/s00211-004-0526-9

Mortari, D. (2017). Least-Squares Solution of Linear Differential Equations. Mathematics, 5.

Mirzaei, 2017, Direct approximation on spheres using generalized moving least squares, BIT Numer. Math., 57, 1041, 10.1007/s10543-017-0659-8

Bos, 2010, Least-squares polynomial approximation on weakly admissible meshes: Disk and triangls, J. Comput. Appl. Math., 235, 660, 10.1016/j.cam.2010.06.019

Liang, 2010, Geometry Optimization with Multilayer Methods Using Least-Squares Minimization, J. Chem. Theory Comput., 6, 3352, 10.1021/ct100453x

Ahn, 2001, Least-squares orthogonal distances fitting of circle, sphere, ellipse, hyperbola, and parabola, Pattern Recognit., 34, 2283, 10.1016/S0031-3203(00)00152-7

Fitzgibbon, 1999, Direct least square fitting of ellipses, IEEE Trans. Pattern Anal. Mach. Intell., 21, 476, 10.1109/34.765658

Ahn, 1999, Geometric least squares fitting of circle and ellipse, Int. J. Pattern Recognit. Artif. Intell., 13, 987, 10.1142/S0218001499000549

Steger, 1998, An unbiased detector of curvilinear structures, IEEE Trans. Pattern Anal. Mach. Intell., 20, 113, 10.1109/34.659930

Qi, 2013, Statistical behavior analysis and precision optimization for the laser stripe center detector based on Steger’s algorithm, Opt. Express, 21, 13442, 10.1364/OE.21.013442

Wang, 2015, Rock Fracture Centerline Extraction based on Hessian Matrix and Steger algorithm, KSII Trans. Internet Inf. Syst., 9, 5073

Stainvas, 2004, A generative model for separating illumination and reflectance from images, J. Mach. Learn. Res., 4, 1499