Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (10): 2795-2806.doi: 10.13229/j.cnki.jdxbgxb.20211384

Previous Articles     Next Articles

Sprocket size measurement method based on machine vision

Hao-jing BAO1(),Si-yuan LIU1(),Zhen REN2,Yun-hui ZHANG1,Zheng-yi HU3,4,Yu-peng GE1   

  1. 1.School of Mechanical and Aerospace Engineering，Jilin University，Changchun 130022，China
    2.School of Mechanical Engineering，Changchun University，Changchun 130022，China
    3.School of Mechanical and Automotive Engineering，South China University of Technology，Guangzhou 510641，China
    4.Production and Education Integration Development Center，Changchun Automobile Industry College，Changchun 130010，China
  • Received:2021-12-16 Online:2023-10-01 Published:2023-12-13
  • Contact: Si-yuan LIU E-mail:emmajingjing@jlu.edu.cn;liusiy@jlu.edu.cn

RICH HTML

 2   

PDF (PC)

75

Abstract:

Due to the limitation of sprocket shape and the requirement of on-site measurement for efficiency， the existing external parameter calibration methods are difficult to apply to the external parameter calibration of the hub face. The paper proposes an external parameter calibration method based on the invariance of quadratic curve. In this method， a cylinder machined with concentric rings is placed in the center hole of the sprocket， and the equation coefficients corresponding to the three circles are calculated by the radius of the large and small circles on the ring and the radius of the hub hole of the sprocket. The external parameters of the hub face can be obtained through the above parameters. According to the shape characteristics of the sprocket tooth profile， a screening model of the highest and lowest points on the tip and root circle regions is proposed， and the diameter is obtained by ellipse fitting using the highest point array and the lowest point array. In the experiment， four sprockets of different sizes are used as the measured objects， and the results obtained by the measurement method in this paper are compared with the results obtained by the three-coordinate measuring instrument. The experimental results show that the error of the measurement method in the paper is less than 40 μm.

Key words: machine vision, sprocket, size measurement, parameter calibration

CLC Number: 

  • TH164

Fig.1

Establishment of world coordinate system basedon end face of sprocket"

Fig.2

Imaging relationship between ring and sprocketend surface"

Fig.3

Spatial relationship between end face of sprocketand measured plane of tooth profile"

Fig.4

Flow chart of screening the highest and lowest points of sprocket （Step2）"

Fig.5

Flowchart of screening the highest and lowestpoints of sprocket （Step3）"

Table 1

Main dimensions of tested sprocket"

编号齿根圆直径齿顶圆直径
1#75.38582.574
2#53.16267.897
3#116.281131.511
4#230.154244.879

Fig.6

Tested sprocket"

Table 2

Dimensions of concentric rings used for calibration"

项目1#2#3#4#

外圆环直径

内圆环直径

9±0.0059±0.00530±0.00530±0.005
5±0.0055±0.00520±0.00520±0.005

Fig.7

Experimental site diagram of sprocket size measurement"

Table 3

Experimental equipment and main parameters"

设备名称设备型号主要参数
计算机DELL-44060SIntel（R） Core（TM）-i5
相机MER-125-30UM分辨率：1292×964 pixel
镜头ComputerM2514-MP焦距：25 mm
背光源CCS LFL-200发光面积：200 mm ×180 mm

Fig.8

Camera internal reference calibration images"

Table 4

Transformation matrix between world coordinates and camera coordinates"

链轮编号坐标变换矩阵
1#R1=????0.9922????0.0888-0.0851-0.0812????0.9925????0.0888?????0.0924-0.0812????0.9922
2#R2=????0.9980????0.0436-0.0436-0.0417????0.9981????0.0436????0.0454-0.0417????0.9980
3#R3=????0.9960????0.0519-0.0523-0.0497????0.9971????0.0435????0.0541-0.0408????0.9970
4#R4=????0.9895????0.0951-0.0957-0.0912????0.9940????0.0456????0.0992-0.0365????0.9936

Fig.9

Coplanar target"

Fig.10

Evaluation test of edge detection algorithm"

Fig.11

Corresponding edge points of tooth root circleand addendum circle， fitting results"

Table 5

Measurement results of sprocket dedendumcircle diameter"

编号标准直径代数椭圆拟合几何椭圆拟合
测量值误差测量值误差

1#

2#

75.385

53.162

75.366

53.124

0.019

0.038

75.364

53.127

0.021

0.035

3#116.281116.2450.036116.2490.032
4#230.154230.1300.024230.1410.013

Table 6

Measurement result of sprocket addendumcircle diameter"

编号标准直径代数椭圆拟合几何椭圆拟合
测量值误差测量值误差

1#

2#

82.574

67.897

82.554

67.859

0.020

0.038

82.556

67.931

0.018

0.034

3#131.511131.4750.036131.5440.033
4#244.879244.8630.016244.8570.022

Table 7

Measuring time of sprocket dedendum circle diameter"

方法圆编号平均时间
1#2#3#4#
代数拟合7.346.457.858.237.47
几何拟合9.188.0810.9211.689.97

Table 8

Measuring time of sprocket addendum circle diameter"

方法圆编号平均时间
1#2#3#4#
代数拟合6.134.966.286.966.08
几何拟合8.267.269.3410.328.80
1 孟繁忠. 链条链轮产品设计与检验[M]. 北京:机械工业出版社, 1996.
2 谭庆昌, 赵洪志. 机械设计[M]. 北京:高等教育出版社, 2008.
3 张国雄. 三坐标测量机[M]. 天津:天津大学出版社,1999.
4 郑国伟.《中国制造2025》简介与相关情况[J].中国仪器仪表,2018(10):25-28.
Zheng Guo-wei. Brief Introduction of made in China 2025 and related information[J]. China Instrumentation, 2018(10): 25-28.
5 Liu Si-yuan, Tan Qing-chang, Zhang Ya-chao. Shaft diameter measurement of using the structured light vision[J]. Sensors, 2015, 15(8): 19750-19767.
6 Gu Peng, Zhu Chuan-min, Yu Yi-qing,et al. Evaluation and prediction of drilling wear based on machine vision[J]. International Journal of Advanced Manufacturing Technology, 2021, 114(7/8): 2055-2074.
7 Sun Chuan-zhi, Wang Bao-sheng, Liu Yong-meng, et al. Design of high accuracy cylindrical profile measurement model for low-pressure turbine shaft of aero engine[J]. Aerospace Science and Technology, 2019, 95: No.105442.
8 Song Li-mei, Ru Yuan, Yang Yan-gang, et al. Full-view three-dimensional measurement of complex surfaces[J]. Optical Engineering, 2018, 57(10): No.104106.
9 田峰. 链轮毛刺检测方法与几何参数测量技术研究[D]. 扬州:扬州大学机械工程学院,2021.
Tian Feng. Research on spocket burr detection method and geometric parameter measurement technology[D]. Yangzhou: School of Mechnical Engineering,Yangzhou University,2021.
10 Ma Yu-zhen, Yu Yong-xin, Wang Xin-hua. Diameter measuring technique based on capacitive probe for deep hole or oblique hole monitoring[J]. Measurement, 2014, 47: 42-44.
11 Md H A, Syuhei K, Kensuke U. Application of machine vision in improving safety and reliability for gear profile measurement[J]. Machine Vision and Applications, 2014(25):1549-1559.
12 Miao Jian-wei, Tan Qing-chang, Liu Si-yuan, et al. Vision measuring method for the involute profile of a gear shaft[J]. Applied Optics, 2020, 59(13): 4183-4190.
13 Kou Ying, Zhang Long-fei, Liu Si-yuan, et al. Aperture measurement based on a conic invariant[J]. Applied Optics, 2022, 61(6): 99-106.
14 Liu Zhen, Wei Xin-guo, Zhang Guang-jun. External parameter calibration of widely distributed vision sensors with non-overlapping fields of view[J]. Optics and Lasers in Engineering, 2013, 51(6): 643-650.
15 Lu R S, Li Y F. A global calibration method for large-scale multi-sensor visual measurement systems[J]. Sensors and Actuators A-Physical, 2004, 116(3): 384-393.
16 Liu Zhen, Zhang Guang-jun, Wei Zhen-zhong,et al. Novel calibration method for non-overlapping multiple vision sensors based on 1D target[J]. Optics and Lasers in Engineering, 2011, 49(4): 570-577.
17 Bao Hao-jing, Tan Qing-chang, Liu Si-yuan, et al. Computervision measurement of pointer meter readings based on inverse perspective mapping[J]. Applied Sciences-Basel, 2019, 9(18): No.9183729.
18 顾国华, 王佳节, 陈钱, 等. 二维旋转平台下的相机参数标定[J]. 光学精密工程, 2017, 25(7): 1890-1899.
Gu Guo-hua, Wang Jia-jie, Chen Qian, et al. Camera parameter calibration based on two-dimensional rotating platform[J]. Optics and Precision Engineering, 2017, 25(7): 1890-1899.
19 Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
20 Grimson W E, Hildreth E C. Digital step edges from zero crossing of second directional derivatives[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1985, 7(1): 121-127.
21 崔家礼, 宫贺, 王一丁, 等. 基于代数距离的椭圆拟合的优化及应用研究[J]. 计算机科学, 2014(2): 88-90.
Cui Jia-li, Gong He, Wang Yi-ding, et al. Optimization and research on ellipse fitting and application based on algebraic distance[J]. Computer Science, 2014(2): 88-90.
22 Walter Gander, Gene H, Golub Rolf Strebel. Least-squares fitting of circles and ellipses[J]. BIT Numerical Mathematics, 1994, 43: 558-578.
[1] Jing-bin LI,Yu-kun YANG,Bao-qin WEN,Za KAN,Wen SUN,Shuo YANG. Method of extraction of navigation path of post-autumn residual film recovery based on stubble detection [J]. Journal of Jilin University(Engineering and Technology Edition), 2021, 51(4): 1528-1539.
[2] LIU En-ze,WU Wen-fu. Agricultural surface multiple feature decision fusion disease judgment algorithm based on machine vision [J]. Journal of Jilin University(Engineering and Technology Edition), 2018, 48(6): 1873-1878.
[3] LIN Jin-hua, WANG Yan-jie, WANG Lu, YAO Yu. Real-time surface reconstruction based global camera pose optimization [J]. 吉林大学学报(工学版), 2018, 48(3): 909-918.
[4] ZHANG Fei, SHAN Zhong-de, REN Yong-xin, NIE Jun-gang, LIU Feng. Calibration of line array camera for head defect detection system on cylinder cover [J]. 吉林大学学报(工学版), 2017, 47(2): 540-545.
[5] ZHOU Xiao-dong, ZHANG Ya-chao, TAN Qing-chang, ZHANG Wei-jun. New method of cylindricity measurement based on structured light vision technology [J]. 吉林大学学报(工学版), 2017, 47(2): 524-529.
[6] ZHANG Bo, WANG Wen-jun, WEE Minkooc, CHENG Bo. Detection handheld phone use by driver based on machine vision [J]. 吉林大学学报(工学版), 2015, 45(5): 1688-1695.
[7] LIU Chang-ying, CAI Wen-jing, WANG Tian-hao, LI Ji-zhi, JIA Yan-mei, SONG Yu-he. Vision inspection technology of fracture splitting notch of auto connecting rod [J]. 吉林大学学报(工学版), 2014, 44(4): 1076-1080.
[8] WAN Chuan, TIAN Yan-tao, LIU Shuai-shi, CHEN Hong-wei. Face tracking and facial expression recognition system based on active machine vision [J]. 吉林大学学报(工学版), 2013, 43(02): 459-465.
[9] WANG Dian-hai, TAO Peng-fei, JIN Sheng, MA Dong-fang. Method of calibrating and validating car-following model [J]. 吉林大学学报(工学版), 2011, 41(增刊1): 59-65.
[10] LI Zhi-long, HUANG Cheng-jie, LI Li-guang, WU Zhi-jun. Simultaneous measurement of particle field and size distribution by two digital cameras [J]. 吉林大学学报(工学版), 2011, 41(05): 1503-1506.
[11] ZHAO Ding-Xuan, CUI Yu-Xin, GONG Jie, NI Tao, HOU Jing-Wei, ZHAO Ying, WANG Chao-Fei. Method of tracking and measuring moving object based on machine vision [J]. 吉林大学学报(工学版), 2010, 40(增刊): 257-0261.
[12] HUANG Cheng-jie,WU Zhi-jun,LI Zhi-long,LI Li-guang. Fundamentals of particle velocity and size measurement in same field and its experimental validation [J]. 吉林大学学报(工学版), 2010, 40(04): 1106-1111.
[13] MENG Xiang-Yan, SUN Yong-Hai, WANG Xiao-Dan, LI Zhi, HU Tie-Jun, HE Bing. Methods of extraction and analysis of grading features of ribeye section of beef carcass [J]. 吉林大学学报(工学版), 2010, 40(02): 485-0490.
[14] YIN Yong-guang,DING Yun. Rapid method for enumeration of Escherichia coli in food based on computer vision [J]. 吉林大学学报(工学版), 2009, 39(增刊2): 344-0348.
[15] WANG Hong-Rui, TIAN Yan-Chao, GU Qing. Adaptive tuning methods for digital camera parameters under variou illuminations [J]. 吉林大学学报(工学版), 2009, 39(05): 1262-1267.
Viewed
Full text


Abstract

Cited
  Shared   
  Discussed   
No Suggested Reading articles found!
Copyright © Journal of Jilin University(Engineering and Technology Edition), All Rights Reserved.
Tel: +86-431-85095297　Fax: +86-431-85094074　E-mail: xbgxb@jlu.edu.cn
Powered by Beijing Magtech Co. Ltd