激光扫描在工件表面检测中的应用方法

龚海强;单奇;吴鹏飞;罗新河

应用光学 ›› 2019, Vol. 40 ›› Issue (4) : 686-691.

应用光学 ›› 2019, Vol. 40 ›› Issue (4) : 686-691. DOI: 10.5768/JAO201940.0407002

激光扫描在工件表面检测中的应用方法

  • 龚海强1, 单奇1, 吴鹏飞1, 罗新河1
作者信息 +

Application of laser scanning in workpiece surface testing

  • GONG Haiqiang1, SHAN Qi1, WU Pengfei1, LUO Xinhe1
Author information +
文章历史 +

摘要

为了获取磨削工件表面特征信息,提出一种基于激光扫描的磨削工件表面检测方法。利用机械臂带动激光传感器扫描放置在激光测量平面中的磨削工件,从而获得工件在激光测量平面中的三维坐标信息,通过相邻2个扫描点之间的高度变化求出工件边界点的三维坐标信息,结合x轴和y轴坐标的极值点利用最小二乘法拟合出工件边界在激光测量平面中的解析式,进一步求出附着在工件上的坐标系相对于激光测量坐标系的位姿,最后利用工件在激光测量坐标系中的位置矢量信息得出其表面特征信息。实验结果表明,利用该方法对工件表面进行检测,得到工件表面检测误差为0.11 mm,检测平均时间在1 s内,满足工件表面特征检测要求。

Abstract

In order to obtain the surface features information of the workpiece to be grinded, a method for surface detection of grinding workpiece based on laser scanning was proposed. The robotic arm was used to drive the laser sensor to scan the grinding workpiece placed in the laser measuring plane, thereby obtaining the three-dimensional coordinates information of the workpiece in the laser measuring plane, and obtaining the characteristic boundary point of the workpiece by the height change between two adjacent scanning points. Then, the extreme points and least squares of the x-axis and y-axis coordinates were combined to fit the analytical expression of the workpiece boundary in the laser measurement plane, and the offset and rotation angle of the workpiece relative to the laser measurement plane were further determined. Finally, the surface features information of the workpiece was obtained by using the position and attitude information of the workpiece in the laser measuring plane. The experimental results show that the detection error of the workpiece surface is 0.11 mm and the average detection time is within 1 s, which meets the requirements of workpiece surface features detection.

关键词

工件检测 / 非接触检测 / 测量 / 激光扫描

Key words

laser scanning / workpiece detection / non-contact detection / measurement

引用本文

导出引用
龚海强, 单奇, 吴鹏飞, 罗新河. 激光扫描在工件表面检测中的应用方法. 应用光学. 2019, 40(4): 686-691 https://doi.org/10.5768/JAO201940.0407002
GONG Haiqiang, SHAN Qi, WU Pengfei, LUO Xinhe. Application of laser scanning in workpiece surface testing. Journal of Applied Optics. 2019, 40(4): 686-691 https://doi.org/10.5768/JAO201940.0407002

基金

国家自然科学基金(51475387)

参考文献

房建国, 马艳玲, 李迪, 等.发动机叶片椭圆进排气边智能磨削加工检测一体化技术[J].航空精密制造技术, 2016, 52(6):1-6.
高尚, 王紫光, 康仁科, 董志刚, 张璧.工件旋转法磨削硅片的磨粒切削深度模型[J].机械工程学报, 2016, 52(17): 86-93.
厉荣宣, 史东东.工件表面裂纹机器视觉检测研究[J].自动化仪表, 2017, 38(9):85-88.
刘永勋, 马秉馨, 赵敬云.大型机床工件的磨削表面粗糙度检测系统[J].仪表技术与传感器, 2018(9):71-74.
杨张利.机器零件内部材质缺陷的无损探伤与自动检测[J].兵器装备工程学报, 2014(6):105-107.
曾碧, 张伟.基于机器视觉的打磨工件跟踪方法研究[J].计算机应用研究, 2018, 35(11):3513-3516.
董明明, 陈戈.基于颜色分离与特征统计分析的工件图像表面异物检测算法[J].国外电子测量技术, 2017, 36(9):45-49.
殷建.基于两路激光实时跟踪的机床刀具位姿误差测量[J].激光与
FANG Jianguo, MA Yanling, LI Di, et al. Intelligent method of machining-measuing integration for aero-engine elliptical blade-edges[J].Aviation Precision Manufacturing Technology, 2016, 52 (6): 1-6.
GAO Shang, WANG Ziguang, KANG Renke, et al. Model of grain depth of cut in wafer rotation grinding method for silicon wafers[J]. Journal of Mechanical Engineering, 2016, 52(17): 86-93.
LI Rongxuan, SHI Dongdong. Research on the workpiece surface crack detection based on machine vision[J]. Process Automation Instrumentation, 2017, 38 (9): 85-88.
SUN X Y, KANG F N, WANG M M.Improved probabilistic neural network PNN and its application to defect recognition in rock bolts[J]. International Journal of Machine Learning and Cybernetics, 2016, 7(5):909-919.
LIU Yongxun, MA Bingxin, ZHAO Jingyun. Surface roughness detection system for workpiece of large machine tool[J]. Instrument Technique and Sensor, 2018(9): 71-74.
YANG Zhangli. Nondestructive inspection and automatic detection of internal material d

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