二值化和孔洞填充融合差分的微精密玻璃封装电连接器同心度检测

doi:10.16180/j.cnki.issn1007-7820.2021.07.011

Abstract

Abstract:

The micro-precision glass encapsulated electrical connector is widely used in the electronic industry. At present, manual detection is mainly used to detect concentricity, but the detection efficiency and accuracy can no longer meet the application needs. To solve the complex surface texture of cylindrical glass-encapsulated electrical connector and the difficulty in determining the boundary contour of high-temperature and low-temperature glass columns, a fusion difference algorithm based on binary and hole-filling is proposed, and clear inner circle image is successfully obtained. The image of the filled hole is processed by the maximum connected area method, the noise caused by the background texture is removed, and the outer circle image of the main body of the electrical connector is obtained. Then, a concentric calculation model is established based on the information of the inner and outer circle coordinates, and the concentricity parameters are obtained. The experimental results show that the time for concentric detection is shortened from more than 6.5 s for manual detection to less than 0.32 s, and the accuracy rate reaches more than 90%.

Key words: glass encapsulated, electrical connector, concentric detection, hole filling, difference operation, inner circle image, maximum area method, the main external circular

CLC Number:

TP391

WANG Gaowei,WANG Fuzhong,LIU Qunpo,WANG Manli,GAO Ruxin,WANG Zhenying. Concentric Detection of Micro-Precision Glass Encapsulated Electrical Connectors by Binary and Hole Filling Fusion Differential[J].Electronic Science and Technology, 2021, 34(7): 61-66.

Figures/Tables 10

Figure 1.

Figure 2.

Figure 3.

Figure 4.

Figure 5.

Figure 6.

Figure 7.

Figure 8.

Figure 9.

Table 1.

References 16

[1]	申振腾, 陈建平, 杜厚栋, 等. 基于机器视觉的绝缘子RTV涂层质量检测方法研究及应用[J]. 电瓷避雷器, 2018(5):233-240.
	Shen Zhenteng, Chen Jianping, Du Houdong, et al. Research and application of RTV coating quality inspection method based on machine vision[J]. Insulators and Surge Arresters, 2008(5):233-240.
[2]	郭延杰, 白福忠, 刘珍, 等. 基于降维Hough变换算法的零件同心度误差测量[J]. 激光杂志, 2015,36(7):27-30.
	Guo Yanjie, Bai Fuzhong, Liu Zhen, et al. Part concentricity error measurement based on dimension-reducing Hough transformation algorithm[J]. Laser Journal, 2015,36(7):27-30.
[3]	Shim J H, Nam T H. Machine vision based automatic measurement algorithm of concentricity for large size mechanical parts[C]. Kuala Lumpur:International Conference on Control Engineering and Artificial Intelligence, 2017.
[4]	沈宝国, 蒋超峰, 蒋修定, 等. 基于机器视觉的零件同心度检测系统的设计[J]. 机械设计与制造, 2017(7):115-117.
	Shen Baoguo, Jiang Chaofeng, Jiang Xiuding, et al. Design of detection system for part's concentricity based on machine vision[J]. Machinery Design & Manufacture, 2017(7):115-117.
[5]	Chenhuei H, Huang Y W, Weng-Jiun W. Automatic concentricity measurement by image processing[C]. Hamamatsu:IEEE Instrumentation and Measurement Technology Conference, 1994.
[6]	崔灿, 景文博, 闫娜, 等. 一种基于机器视觉的轴承内外径和同心度检测方法[J]. 长春理工大学学报(自然科学版), 2017,40(3):75-79.
	Cui Can, Jing Wenbo, Yan Na, et al. A method for inner and outer diameter and concentricity inspection of bearings based on machine[J]. Journal of Chanchun University of Science and Technology(Natural Science), 2017,40(3):75-79.
[7]	陈永清, 蒋惠芬, 范水明, 等. 计算机视觉的同心度实验平台设计[J]. 实验室研究与探索, 2016,35(9):59-62.
	Chen Yongqing, Jiang Huifen, Fan Shuiming, et al. Design of concentricity experiment platform based on computer vision[J]. Research and Exploration in Laboratory, 2016,35(9):59-62.
[8]	程伟, 惠珑伟, 王磊. 装卸料机主提升同心度问题分析与处理[J]. 起重运输机械, 2019,5(3):61-65.
	Chen Wei, Hui Longwei, Wang Lei. Analysis and treatment of lifting concentricity of loading and unloading machine[J]. Analysis Research, 2019,5(3):61-65.
[9]	Lu X, Jiang F, Ye R, et al. Measuring concentricity and coaxial tolerance of nozzle and cavity with tool microscope[C]. Xiamen:The Seventh International Symposium on Precision Mechanical Measurements, 2016.
[10]	Zhang Y, Wang Y, Liu Y, et al. A concentricity measurement method for large forgings based on laser ranging principle[J]. Measurement, 2019,147(3):106838-106851. doi: 10.1016/j.measurement.2019.07.066
[11]	邹秀阳, 沈晓宇. 一种基于图像处理的同轴度误差测量方法[J]. 电子科技, 2016,29(5):175-177.
	Zou Xiuyang, Shen Xiaoyu. A new coaxial error measurement method based on image processing[J]. Electronic Science and technology, 2016,29(5):175-177.
[12]	豆永坤. 基于机器视觉的机械零件几何外形检测研究[D]. 兰州:兰州理工大学, 2018.
	Dou Yongkun. Research on geometrical inspection of mechanical parts based on machine vision[D]. Lanzhou:Lanzhou University of Technology, 2018.
[13]	郝华东, 施浩磊, 吴泽南, 等. 基于结构光三维扫描技术的小尺寸轴同轴度精密测量方法研究[J]. 中国测试, 2018,44(2):36-40.
	Hao Huadong, Shi Haolei, Wu Zenan, et al. Research on precision measuring method of small shaft coaxiality based on structured light 3D scanning technology[J]. China Measurement & Test, 2018,44(2):36-40.
[14]	王世豪, 蔡延光, 蔡颢. 玻璃表面缺陷检测算法研究[J]. 东莞理工学院学报, 2018,25(1):45-49.
	Wang Shihao, Cai Yanguang, Cai Hao. Research on flaw detection algorithm of glass surface[J]. Journal of Dongguan University of Technology, 2018,25(1):45-49.
[15]	卢小平, 张航, 张冬梅, 等. 一种基于无人机影像的迭代二值化道路裂缝检测方法[J]. 河南理工大学学报(自然科学版), 2019,38(6):55-59.
	Lu Xiaoping, Zhang Hang, Zhang Dongmei, et al. An iterative binary road crack detection method based on UAV image[J]. Journal of Henan Polytechnic University(Natural Science), 2019,38(6):55-59.
[16]	姜维刚, 王仲霖, 潘飞. 一种基于孔洞填充的路面裂缝识别方法[J]. 电子技术与软件工程, 2019(11):62.
	Jiang Weigang, Wang Zhonglin, Pan Fei. A pavement crack identification method based on hole filling[J]. Image & Multimedia Technology, 2019(11):62.

标号	外圆圆心/像素	内圆圆心/像素	同心度 /mm	纯人工检测结果
1	(328.19,256.03)	(319.51,253.30)	0.010 6	PASS
2	(319.512,53.30)	(319.47,248.30)	0.038 0	PASS
3	(318.55,248.84)	(326.59,254.20)	0.073 5	PASS
4	(318.85,254.31)	(329.39,245.20)	0.105 9	FAIL
5	(305.29,235.67)	(314.18,231.05)	0.076 2	PASS
6	(316.71,253.48)	(328.12,250.30)	0.090 1	FAIL
7	(323.13,244.65)	(332.64,241.20)	0.076 9	FAIL
8	(319.78,250.15)	(321.68,247.50)	0.024 8	PASS
9	(325.81,244.88)	(329.69,245.09)	0.029 6	PASS
10	(314.57,256.26)	(316.11,252.02)	0.034 3	PASS

Concentric Detection of Micro-Precision Glass Encapsulated Electrical Connectors by Binary and Hole Filling Fusion Differential

RichHTML

PDF (PC)

Like

Knowledge

Abstract

Cite this article

share this article

Figures/Tables 10

References 16

Related Articles 1

Metrics

Comments

Recommended 9