利用图像处理技术测量丝杠螺距的机器视觉系统

doi:10.6040/j.issn.1672-3961.0.2020.509

摘要/Abstract

摘要：

为解决接触式测量方式损伤工件以及测量设备造价昂贵的问题, 设计一种基于机器视觉的低成本、高精度的丝杠螺距测量系统。该系统依靠MATLAB平台搭建, 由图像采集系统、机械系统、步进控制系统、图像和数据处理4个子系统构成。利用亚像素图像拼接算法拼接丝杠子图像从而获得完整的丝杠图像, 通过图像增强、降噪等预处理方法改善图像质量, 采用形态学边缘提取和亚像素边缘定位的组合算法精确提取丝杠边缘, 并由数据处理程序自动测量螺距误差。试验结果表明, 机器视觉系统和标准仪器(JCS-040A)对3种型号滚珠丝杠螺距测量结果的相对误差分别不超过0.04%、0.06%、0.09%, 该方法应用于丝杠工业测量中的可行性得以验证。

关键词: 丝杠, 机器视觉, 螺距误差, 图像拼接, 图像处理

Abstract:

In order to solve problems of damage to workpiece and high-cost of measuring equipment by contact measuring method, a low-cost and high-precision screw pitch measuring system based on machine vision was designed. The system was built on MATLAB and consisted of four sub-systems: image acquisition system, mechanical system, stepping control system, image and data processing system. Sub-pixel mosaic algorithm was used to splice the sub-images of the lead screw and a complete lead screw image could be obtained. Image quality was improved by image enhancement and filtering. Morphological edge extraction and sub-pixel edge location algorithm were used to extract the screw edge accurately, and pitch errors were measured automatically by data processing program. The experimental results showed that the relative error of the machine vision system and standard instrument (JCS-040A) for the measurement results of the three types of ball screw pitches didn't exceed 0.04%、0.06%、0.09%, respectively. The feasibility of the system in industrial measurement of lead screw was validated.

Key words: screw, machine vision, pitch error, image mosaic, image processing

中图分类号:

TH136

韩天雨,路长厚,李建美,尹昂,侯秋林. 利用图像处理技术测量丝杠螺距的机器视觉系统[J]. 山东大学学报 (工学版), 2022, 52(3): 80-85.

Tianyu HAN,Changhou LU,Jianmei LI,Ang YIN,Qiulin HOU. A machine vision system for measuring screw pitch with image processing techniques[J]. Journal of Shandong University(Engineering Science), 2022, 52(3): 80-85.

图/表 11

图1

图2

图3

图4

图5

图6

图7

表1

图8

图9

图10

参考文献 15

1	焦洁, 肖正义. 12 m激光滚珠丝杠(副)动态行程测量仪的研制[J]. 设计与研究, 2013, (5): 72- 77.
	JIAO Jie , XIAO Zhengyi . Development of 12m laser ball screw dynamic stroke measuring instrument[J]. Design and Research, 2013, (5): 72- 77.
2	GADELMAWLA E S . Computer vision algorithms for measurement and inspection of external screw threads[J]. Measurement, 2017, (100): 36- 49.
3	GADELMAWLA E S . Computer vision algorithms for measurement and inspection of spur gears[J]. Measurement, 2011, (44): 546- 556.
4	GADELMAWLA E S . A novel system for automatic measurement and inspection of screw threads[J]. Eng Manuf IMECH E: Part B, 2004, 2018 (B5): 545- 556.
5	ZHAO Leilei , FENG Hutian , RONG Qianfeng . A novel non-contact measuring system for the thread profile of a ball screw[J]. Mechanical Science, 2018, (9): 15- 24.
6	JING Min. Comprehensive measurement system for screw thread parameter based on machine vision[C]//Processdings of I.T. S Engineering College46. Noida, India: ICCCS, 2015: 266-270.
7	STEGER Carsten , URICH Markus , WIEDEMANN Christian . Machine vision algorithms and applications[M]. Berlin, Germany: Wiley-VCH, 2007: 279- 283.
8	郑艳飞. 基于区域配准的图像拼接算法[D]. 济南: 山东大学, 2012.
	ZHENG Yanfei. Image mosaic algorithm based on regional registration[D]. Jinan: Shandong University, 2012.
9	WEI Shoude , LAI Shanghong . Fast template matching based on normalized cross correlation with adaptive multilevel winner update[J]. IEEE Transactions on Image Processing, 2008, 17 (11): 2227- 2235.
10	KUGLIN C D, HINES D C. The phase correlation image alignment method[C]//Processdings of Cybernetics and Society. New York, USA: IEEE, 1975: 163-165.
11	周虎. 高精度视觉测量系统关键技术研究与应用[D]. 上海: 东华大学, 2011.
	ZHOU Hu. Research and application key technology for high precision image measurement system[D]. Shanghai: Donghua University, 2011.
12	侯秋林, 孙杰, 黄攀凌, 等. 基于机器视觉刀具几何参数检测算法与误差分析[J]. 山东大学学报(工学版), 2017, 47 (4): 78- 82.
	HOU Qiulin , SUN Jie , HUANG Panling , et al. Algorithm and error analysis of tool geometric parameters detection based on machine vision[J]. Journal of Shandong University (Engineering Science), 2017, 47 (4): 78- 82.
13	周策策. 基于机器视觉的螺纹参数测量系统[D]. 天津: 天津大学, 2015.
	ZHOU Cece. Thread parameter measuring system based on machine vision[D]. Tianjin: Tianjin University, 2015.
14	于梦竹. 基于亚像素边缘检测的图像放大算法研究与实现[D]. 哈尔滨: 哈尔滨工程大学, 2013.
	YU Mengzhu. Research on image magnification algorithm based on sub-pixel edge detection[D]. Harbin: Harbin Engineering University, 2013.
15	厉超. 三米滚珠丝杠螺距精度动态测量系统研究[D]. 济南: 山东大学, 2007.
	LI Chao. Study on dynamic measurement system of pitch precision of 3-meter ball screw[D]. Jinan: Shandong University, 2007.

相关文章 15

[1]	宋怀雷,邬忠虎,李利平,娄义黎,孙文吉斌,刘镐,左宇军. 基于数字图像的微观尺度下方解石脉对页岩各向异性的影响[J]. 山东大学学报 (工学版), 2021, 51(5): 91-99, 106.
[2]	蔡国永,贺歆灏,储阳阳. 基于空间注意力和卷积神经网络的视觉情感分析[J]. 山东大学学报 (工学版), 2020, 50(4): 8-13.
[3]	刘保成,朴燕,宋雪梅. 联合检测的自适应融合目标跟踪[J]. 山东大学学报 (工学版), 2020, 50(3): 51-57.
[4]	宋士奇,朴燕,蒋泽新. 基于改进YOLOv3的复杂场景车辆分类与跟踪[J]. 山东大学学报 (工学版), 2020, 50(2): 27-33.
[5]	张宪红,张春蕊. 基于六维前馈神经网络模型的图像增强算法[J]. 山东大学学报(工学版), 2018, 48(4): 10-19.
[6]	侯秋林,孙杰,皇攀凌,孙超,牟文平. 基于机器视觉刀具几何参数检测算法与误差分析[J]. 山东大学学报(工学版), 2017, 47(4): 77-82.
[7]	孟令恒,丁世飞. 基于单静态图像的深度感知模型[J]. 山东大学学报(工学版), 2016, 46(3): 37-43.
[8]	邱晓欣1,2,张文强1,2*,秦晋贤1,2,杜正阳1,2,张德峰1,2. 恶劣环境下多目标实时跟踪算法研究[J]. 山东大学学报(工学版), 2014, 44(2): 21-27.
[9]	高大龙,黄雅平*,李清勇,王胜春,罗四维. 基于列车前向运动视频的全景图拼接算法[J]. 山东大学学报(工学版), 2013, 43(6): 1-6.
[10]	潘晟旻1,2,钟毅1*,王建华2. 基于改进Canny算子的坯料挤压变形边缘提取[J]. 山东大学学报(工学版), 2013, 43(5): 19-23.
[11]	徐姗姗,刘应安*,徐昇. 基于卷积神经网络的木材缺陷识别[J]. 山东大学学报(工学版), 2013, 43(2): 23-28.
[12]	王玉英1,张西忠2,杨森2. 基于新型矢量排序的soft多结构形态学彩色图像处理[J]. 山东大学学报(工学版), 2011, 41(2): 18-22.
[13]	李岳阳,王士同. 基于鲁棒性神经模糊网络的脉冲噪声滤波算法[J]. 山东大学学报(工学版), 2010, 40(5): 164-170.
[14]	浦剑1 ,张军平1 ，黄华2 . 超分辨率算法研究综述[J]. 山东大学学报(工学版), 2009, 39(1): 27-32.
[15]	姚福安,庞向坤,焦营营,王忠林,张锡满 . 基于三色法和BP神经网络的回转窑温度检测[J]. 山东大学学报(工学版), 2008, 38(2): 61-65 .

多维度评价

Viewed

Full text

Abstract

Cited

Shared

Discussed

丝杠型号	公称导程/ mm	公称直径/ mm	精度等级	丝杠全长/mm	实测长度/mm	任意2Π行程内行程变动量最大偏差e_p/μm	指定行程内最大允差e_p/μm
4210#	10	40	4	1002	355	7	18
2505-588-34#	5	25	3	588	375	6	13
3210#	10	30	4	600	370	7	18