一种新型钢球表面检测方法的分析与系统实现

孙虹; 王仲; 付鲁华; 邢芊

doi:10.13433/j.cnki.1003-8728.2016.0121

一种新型钢球表面检测方法的分析与系统实现

doi: 10.13433/j.cnki.1003-8728.2016.0121

孙虹^1,2,
王仲^1,2,
付鲁华^1,2, ,,
邢芊^1,2

1.
天津大学精密测试技术及仪器国家重点实验室, 天津 300072;
2.
天津大学微光机电系统技术教育部重点实验室, 天津 300072

基金项目:

国家重大科学仪器设备开发专项项目(2013YQ17053903)资助

详细信息

作者简介:
孙虹(1989-),硕士,研究方向为工业控制、工业在线检测,sunhong0821@qq.com

通讯作者:
付鲁华,副教授,博士,fuluhua@tju.edu.cn

计量
- 文章访问数: 196
- HTML全文浏览量: 28
- PDF下载量: 10
- 被引次数: 0
出版历程
- 收稿日期: 2014-02-19
- 刊出日期: 2016-01-05

Analysis and Implementation of Novel Method for Steel Ball Surface Detection

Sun Hong^1,2,
Wang Zhong^1,2,
Fu Luhua^{1,2
, ,},
Xing Qian^1,2

1.
State Key Laboratory of Precision Measuring Technology and Instrument, Tianjin University, Tianjin 300072, China;
2.
Moems Education Ministry Key Laboratory, Tianjin University, Tianjin 300072, China

摘要

摘要: 钢球作为常用的球轴承滚动体,其表面质量直接影响轴承的性能及寿命。目前已研制出的钢球表面缺陷检测装置展开机构结构复杂,不适用于大批量检测。基于一种双图像传感器的球面全展开方法,分析了在球列状态下球面完全展开所需要满足的参数条件。搭建系统进行实验,实验结果表明,该系统利用双传感器同时分别对每粒钢球进行5次采像就能够对钢球表面进行精确有效的高速检测与分选,漏检率低,检测速度可达到14400粒/h,具有广泛的应用前景。
- 钢球 /
- 缺陷检测 /
- 球面展开
Abstract: Steel ball is the rolling element of the ball bearings, whose surface quality directly affect the performance and longevity of the bearing. The developed systems of steel ball surface damage detection have complex unfolding mechanisms, which is not suitable for mass inspection. This paper is based on the sphere surface unfolding method with double image sensors, and the parameters that can fully unfold the sphere surface when the balls are analyzed in a queue. A system is built to illustrate the feasibility of this method. The result shows that the system accurately detect and sort the balls at a high speed of 14,400 pcs/h only take 5 images with each image sensor.
- ball bearings /
- CCD cameras /
- damage detection

HTML全文

参考文献(13)

[1]	Kotzalas M N, Harris T A. Fatigue failure and ball bearing friction[J]. Tribology Transactions, 2000,43(1):137-143
[2]	杨东林,于正林.轴承钢球表面缺陷的快速检测方法[J].兵工学报,2009,30(6):797-802 Yang D L, Yu Z L. Rapid detection method of surface defects of steel ball for bearing[J]. Acta Armamentarii, 2009,30(6):797-802(in Chinese)
[3]	杜玉军,高明,辛维娟,等.基于机器视觉的闭气塞表面缺陷自动检测系统[J].国外电子测量技术,2011,30(4):13-16 Du Y J, Gao M, Xin W J, et al. Automatic detection system of surface defect for gas lock valve based on machine vision[J]. Foreign Electronic Measurement Technology, 2011,30(4):13-16(in Chinese)
[4]	刘青,张进华,黄军勤.基于机器视觉的钢球表面缺陷检测和分类[J].轴承,2013,(10):44-48 Liu Q, Zhang J H, Huang J Q. Detection and classification for surface defects of steel balls based on machine vision[J]. Bearing, 2013,(10):44-48(in Chinese)
[5]	陈世利,常文爽,靳世久.汽轮机轮缘超声相控阵检测中缺陷方向识别[J].纳米技术与精密工程,2013,11(4):328-333 Chen S L, Chang W S, Jin S J. Recognition of defect direction in ultrasonic phased array testing of turbine disc rims[J]. Nanotechnology and Precision Engineering, 2013,11(4):328-333(in Chinese)
[6]	刘泽,王嵬,王平.钢轨表面缺陷检测机器视觉系统的设计[J].电子测量与仪器学报,2010,24(11):1012-1017 Liu Z, Wang W, Wang P. Design of machine vision system for inspection of rail surface defects[J]. Journal of Electronic Measurement and Instrument, 2010,24(11):1012-1017(in Chinese)
[7]	Wang P, Zhao Y L, Liu X L, et al. The key technology research for vision inspecting instrument of steel ball surface defect[J]. Key Engineering Materials, 2009,392-394:816-820
[8]	栗琳,王仲,裴芳莹,等.双图像传感器的球表面展开方法[J].仪器仪表学报,2012,33(7):1641-1646 Li L, Wang Z, Pei F Y, et al. Sphere surface unfolding method with two image sensors[J]. Chinese Journal of Scientific Instrument, 2012,33(7):1641-1646(in Chinese)
[9]	Do Y, Lee S, Kim Y. Vision-based surface defect inspection of metal balls[J]. Measurement Science and Technology, 2011,22(10):107001
[10]	Li J H, Parker J M, Hou Z. An intelligent system for real time automatic defect inspection on specular coated surfaces[C]//Proceedings of the SPIE Visual Communications and Image Processing, Beijing:SPIE, 2005:596043-1-596043-9
[11]	潘洪平,董申,梁迎春,等.钢球表面缺陷的自动检测与识别[J].中国机械工程,2001,12(4):369-372 Pan H P, Dong S, Liang Y C, et al. Automatic detection and recogition of steel ball's surface flaws[J]. China Mechanical Engineering, 2001,12(4):369-372(in Chinese)
[12]	Ng T W. Optical inspection of ball bearing defects[J]. Measurement Science and Technology, 2007,18(9):N73
[13]	Shen H, Li S X, Gu D Y, et al. Bearing defect inspection based on machine vision[J]. Measurement, 2012,45(4):719-733