机械视觉疲劳裂纹扩展试件自动定位安装方法
作者:
作者单位:

浙江工业大学 特种装备制造与先进加工技术教育部/浙江省重点实验室,浙江工业大学 特种装备制造与先进加工技术教育部/浙江省重点实验室,浙江工业大学 特种装备制造与先进加工技术教育部/浙江省重点实验室,浙江工业大学 特种装备制造与先进加工技术教育部/浙江省重点实验室

中图分类号:

TP87 TP317.4


The installation and position method of fatigue crack propagation specimen Based on Machine Vision Technology
Author:
Affiliation:

Key Laboratory of E M,Ministry of Education Zhejiang Province,Zhejiang University of Technology,Key Laboratory of E M,Ministry of Education Zhejiang Province,Zhejiang University of Technology,Key Laboratory of E M,Ministry of Education Zhejiang Province,Zhejiang University of Technology,Key Laboratory of E M,Ministry of Education Zhejiang Province,Zhejiang University of Technology

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    摘要:

    针对现有疲劳裂纹扩展试验中试件的安装定位仍采用操作繁琐、效率低下的手动安装问题,本文提出了一种基于机械视觉的疲劳裂纹扩展试件安装定位方法,首先,对试件及夹具图像进行采集、处理和分析,实现夹具、试件圆心坐标及半径的自动快速识别、孔差距离的精确测量;然后利用模糊PID算法来控制直流伺服电机运动定位,从而实现试件的正确安装定位;最后采用工业数字显微镜对不同时间点的试件位置偏差进行停机测量对比,实验结果表明:所提出方法能够对试件进行精确安装定位,最大偏差为0.122 mm,具有重要的理论和应用价值,并为其它类似基于机器视觉的检测定位方法提供了有价值的参考。

    Abstract:

    Aiming at the work tedious repetitive and low efficiency in the installation and position of specimen during the high frequency fatigue crack test, the installation and position method of fatigue crack propagation specimen was proposed, which can recognize the center coordinates and radius of fixture and specimen automatically and quickly, measure the holes distance accurately. then the fuzzy PID was used to control the DC servo motor. In order to validate the accuracy of the fatigue crack propagation specimen installation and position system, the digital microscope was used to measure the specimen position deviation at different time points. The research results show that the proposed method can measure the holes distance between fixture and specimen accurately, the maximum deviation is 0.122mm. All above, the presented method is valid and accurate, which provides a valuable reference for other detecting and positioning systems based on machine vision., and it has theoretical significance and practical value.

    参考文献
    [1] 李晓舟,于化东,于占江,等.微小尺寸零件表面缺陷光学检测方法[J].兵工学报,2011,32(7):872-877.
    [2] 云艳,高红俐,沈姗姗.基于机器视觉技术的疲劳裂纹自动检测实验系统[J].机电工程,2012,29(2):183-187.
    [3] 陈曼龙.基于机器视觉的锥螺纹参数测量方法[J].计算机测量与控制,2012,20(5):1166-1168.
    [4] Hao Shen, Shuxiao Li, Duoyu Gu, et al.Bearing defect inspection based on machine vision[J].Measurement,2012,45:719-733.
    [5] S.Supriadi,K.Manabe.Enhancement of dimensional accurancy of dieless tube-drawing process with vision-based fuzzy control[J].Journal of Materials processing Technology,2013,213(6):905-912.
    [6] 周见行,高红俐,齐子诚,等.基于摄像头自动跟踪定位的疲劳裂纹在线测量方法研究[J].中国机械工程,2011,22(11):1302-1306.
    [7] 刘发英,苏秀苹,杨振宇.异形转轴机械特性测试孔的机器视觉定位方法[J].计算机辅助设计与图形学学报,2014,26(5):806-811.
    [8] Du-Ming Tsai,Ming-Chin Lin. Machine-vision-based identification for wafer tracking in solar cell manufacturing [J].Robotics and Computer-Integrated Manufacturing,2013,29:312-321.
    [9] 高红俐,郑欢斌,刘欢,等.高频谐振载荷作用下疲劳裂纹尖端变形场分析[J].浙江工业大学学报,2015,43(2):190-196.
    [10] 孙春凤,袁峰,丁振良.一种新的边缘保持局部自适应图像插值算法[J].仪器仪表学报,2010,31(10):2279-2284.
    [11] 杨顺辽,张正炳.基于图像处理的棚车车门状态自动检测[J].计算机测量与控制,2012,20(10):2606-2608.
    [12] 高红俐.机器视觉谐振式疲劳裂纹扩展试验系统研究[D].杭州:浙江工业大学,2013.
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郑欢斌,高红俐,刘欢,刘辉.机械视觉疲劳裂纹扩展试件自动定位安装方法计算机测量与控制[J].,2015,23(12):78.

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  • 收稿日期:2015-06-29
  • 最后修改日期:2015-08-12
  • 录用日期:2015-08-13
  • 在线发布日期: 2016-01-08
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