Journal of Jilin University(Engineering and Technology Edition) ›› 2023, Vol. 53 ›› Issue (12): 3358-3366.doi: 10.13229/j.cnki.jdxbgxb.20220050

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Flatness error measurement method based on line structured light vision

Si-yuan LIU1(),Yue-qian HOU2,Ying KOU1(),Zhen REN2,Zheng-yi HU3,4,Xue-wei ZHAO2,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:2022-01-11 Online:2023-12-01 Published:2024-01-12
  • Contact: Ying KOU E-mail:liusiy@jlu.edu.cn;kouy@jlu.edu.cn

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Abstract:

Flatness is an important shape deviation. A flatness error measurement method was proposed based on line structured light vision technology for flatness measurement in the field of mechanical component manufacturing and processing. Firstly， the images of light strips were collected， and the spatial coordinates of the scanning points were obtained by the corresponding light plane equation for each position. Secondly， the evaluation methods for flatness errors in national standards was analyzed and a measurement algorithm for flatness errors was established based on geometric constraints. Finally， through the proposed method， the evaluation base plane and flatness errors were calculated using the spatial coordinates of the scanning points. In the experiment， the positioning surfaces of the insert molds are selected as the measured planes， and the measurement results obtained by visual measurement are compared with those obtained by the contact measurement method. The measurement error is less than 20 μm. The experimental results show that the measurement method proposed is feasible and improves the measurement efficiency of flatness error.

Key words: machine vision, flatness, line structured light vision, tolerance measurement

CLC Number: 

  • TH164

Fig.1

Calibration model of the light plane of the line structured light"

Fig.2

Flatness measurement model based on least squares"

Fig.3

Solution model for evaluating the initial value of the base surface"

Fig.4

Tested insert molds"

Fig.5

Flatness measurement experiment"

Table 1

Model and parameters of experimental equipment"

实验设备型号主要参数
计算机联想R5000处理器：AMD R4800u
摄像机恒大水星系列-125-30UM分辨率：1292×964 像素
镜头Computer M2514-MP焦距：25 mm
线激光器LH650-80-3功率：0~20 mW；波长：650 nm
平行辅助光源CCS LFL-200最大亮度：800尼特
标定板NANO CBC 25mm-2.0精度：±1.0 μm

Fig.6

Scanned images of the measured plane （mold 1）"

Fig.7

Scanned images of the measured plane （mold 2）"

Table 2

Calibration results of the light plane at each position of mold 1"

位置编号光平面方程
19.1927XC-0.01465YC+2.8465ZC-1000=0
29.1902XC-0.01465YC+2.846ZC-1000=0
39.3838XC-0.01530YC+2.915ZC-1000=0
49.6735XC-0.01456YC+3.116ZC-1000=0
59.889XC-0.01589YC+3.270ZC-1000=0
610.2258XC-0.017808YC+3.369ZC-1000=0
710.4496XC-0.01926YC+3.463ZC-1000=0
810.6854XC-0.01998YC+3.474ZC-1000=0
910.9072XC-0.01804YC+3.587ZC-1000=0
1011.107XC-0.01869YC+3.574ZC-1000=0

Table 3

Calibration results of light plane at each position of mold 2"

位置编号光平面方程
18.0042XC-0.01546YC+2.897ZC-1000=0
28.1717XC-0.01464YC+2.933ZC-1000=0
38.3115XC-0.01380YC+3.005ZC-1000=0
48.521XC-0.01560YC+3.071ZC-1000=0
58.6764XC-0.01696YC+3.089ZC-1000=0
68.9311XC-0.01617YC+3.209ZC-1000=0
79.1270XC-0.01544YC+3.293ZC-1000=0
89.4170XC-0.01576YC+3.355ZC-1000=0
99.6412XC-0.01472YC+3.386ZC-1000=0
109.8509XC-0.01873YC+3.446ZC-1000=0

Fig.8

Solve and evaluate the images of the initial value of the base surface and the position of the center point of the light strip （mold 1）"

Table 4

Space equation of the optimized initial value of the evaluation base level and the space equation of intersecting plane"

光平面方程
位置1（模具1）2.1456XC+0.2233YC+1.8771ZC-1000=0
位置2（模具1）2.1650XC-0.1990YC+2.0237ZC-1000=0
评定基面（模具1）-0.0316XC+0.09YC-ZC+529.661=0
位置1（模具2）2.1607XC-0.2245YC+1.8694ZC-1000=0
位置2（模具2）2.1925XC+0.2151YC+2.0707ZC-1000=0
评定基面（模具2）-0.0258XC+0.0898YC-ZC+531.6321=0

Table 5

Measurement result of flatness error of mold positioning surface"

接触式方法1误差方法2误差方法3误差
模具10.0080.0130.0050.0250.0170.0190.011
模具20.0070.0150.0080.0200.0130.0170.010
均值-0.0140.0070.0230.0150.0180.011

Table 6

Measurement time of flatness error of mold positioning surface"

几何约束法最小二乘法最小包容法
均值1.6940.8750.625
模具11.6530.8760.635
模具21.7350.8730.615

Fig.9

Scanned images of the measured plane after adding noise （mold 1）"

Fig.10

Scanned images of the measured plane after adding noise （mold 2）"

Table 7

Measurement result of flatness error of mold positioning surface after increasing noise"

接触式方法1（误差）方法2（误差）方法3（误差）
均值-0.026（0.018）0.033（0.025）0.035（0.026）
模具10.0080.025（0.017）0.035（0.027）0.034（0.026）
模具20.0070.026（0.019）0.030（0.023）0.035（0.026）
1 薛岩, 于明. 机械加工精度测量与质量控制[M]. 3版. 北京: 化学工业出版社, 2016.
2 李影. 光电水平仪设计与平面度误差测量方法的研究[D]. 大连:大连理工大学机械工程学院, 2019.
Li Ying. The design of photoelectric level gauge and the study of flatness error measurement method[D]. Dalian: School of Mechanical Engineering, Dalian University of Technology, 2019.
3 谯梁, 冉庆, 龚柯安. 激光平面移相干涉仪示值误差测量结果不确定度评定[J]. 工业计量, 2020, 30(): 66-67.
Qiao Liang, Ran Qing, Gong Ke-an. Evaluation of uncertainty in measurement results of indication error of laser planar phase-shifting interferometer[J]. Industrial Metrology, 2020, 30(Sup.1): 66-67.
4 Wang Han-bin. Uncertainty evaluation for the CMM flatness measurement[J]. Metrology & Measurement Technique, 2019, 46(3): 98-99.
5 单忠德, 张飞, 聂军刚, 等. 非接触式缸盖平面度误差检测方法与测量系统研究[J]. 机械工程学报, 2016, 52(20): 1-7.
Shan Zhong-de, Zhang Fei, Nie Jun-gang, et al. Study on the detection method and measurement system of head flatness error on non-contact detection[J]. Journal of Mechanical Engineering, 2016, 52(20): 1-7.
6 Tan Qing-chang, Kou Ying, Miao Jian-wei, et al. A model of diameter measurement based on the machine vision[J]. Symmetry, 2021, 13(2): No. 187.
7 Liu Rui-yuan, Wang Ze-yuan, Liu Xiao-min, et al. Research on Visual Inspection of Appearance Defects of Automotive Precision Parts, 2020, 41(3): 192-196.
8 Mikko Makela, Marja Rissanen, Herbert Sixta. Machine vision estimates the polyester content in recyclable waste textiles[J]. Resources Conservation and Recycling, 2020, 161: No. 105007.
9 张广军. 机器视觉[M]. 北京:科学出版社, 2005.
10 Liu Bin, Wang Peng, Zeng Yong, et al. Measuring method for micro-diameter based on structured-light vision technology[J]. Chinese Optics Letters, 2010, 8(7): 666-669.
11 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.
12 Zhong Chu-qian, Gao Zhan, Wang Xu, et al. Structured light three-dimensional measurement based on machine learning[J]. Sensors, 2019, 19(14): No. 3229.
13 何文彦, 曹学东, 匡龙. 结构光检测大型止推环止推面平面度探究[J]. 光电工程, 2016, 43(11): 7-12.
He Wen-yan, Cao Xue-dong, Kuang Long, et al. A preliminary based on structured-light for flatness measurement of large annular planes[J]. Opto-Electronic Engineering, 2016, 43(11): 7-12.
14 Miao Hui-si, Xiao Chang-yan, Li Yu-cheng, et al. Machine vision system for key-cap flatness measurement of computer keyboards[J]. Optical Engineering, 2020, 59(3): No.1.
15 谭文, 方淼, 段峰, 等. 基于机器视觉的3D激光平面度测量系统的研究与应用[J]. 仪器仪表学报, 2020, 41(1): 241-249.
Tan Wen, Fang Miao, Duan Feng, et al. Research and application of 3D laser flatness measurement system based on machine vision[J]. Chinese Journal of Scientific Instrument, 2020, 41(1): 241-249.
16 Chen Sheng-feng, Su Chong-yang, Liu Jian, et al. Flatness measurement of platform screen system welding assembly using stereo vision and grid pattern projector[J]. IEEE Sensors Journal, 2022, 22(1): 948-958.
17 . 产品几何量技术规范(GPS)平面度误差检测 [S].
18 Zhang Z. A flexible new technique for camera calibration[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 2000, 22(11): 1330-1334.
19 Steger C. An unbiased detector of curvilinear structures[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1998, 20(2): 113-125.
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