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薄板机器人自动焊接焊枪三维偏差的有效提取

余卓骅, 胡艳梅, 何银水

余卓骅, 胡艳梅, 何银水. 薄板机器人自动焊接焊枪三维偏差的有效提取[J]. 焊接学报, 2019, 40(11): 49-53. DOI: 10.12073/j.hjxb.2019400287
引用本文: 余卓骅, 胡艳梅, 何银水. 薄板机器人自动焊接焊枪三维偏差的有效提取[J]. 焊接学报, 2019, 40(11): 49-53. DOI: 10.12073/j.hjxb.2019400287
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YU Zhuohua, HU Yanmei, HE Yinshui. Effective three-dimensional deviation extraction of the welding torch for robotic arc welding with steel sheets[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 49-53. DOI: 10.12073/j.hjxb.2019400287
Citation: YU Zhuohua, HU Yanmei, HE Yinshui. Effective three-dimensional deviation extraction of the welding torch for robotic arc welding with steel sheets[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2019, 40(11): 49-53. DOI: 10.12073/j.hjxb.2019400287
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薄板机器人自动焊接焊枪三维偏差的有效提取

  • 1.

    华东交通大学理工学院, 南昌 330100

  • 2.

    南昌大学, 南昌 330031

基金项目: 国家自然科学基金资助项目（51665037）

Effective three-dimensional deviation extraction of the welding torch for robotic arc welding with steel sheets

  • 1.

    Institute of Technology. East China Jiao Tong University, Nanchang 330100, China

  • 2.

    Nanchang University, Nanchang 330031, China

摘要

    摘要
  • 摘要: 机器人自动化焊接中焊枪三维偏差的提取是实现自动纠偏的前提.薄板机器人MAG对接焊接中对试件开坡口，试验中利用被动视觉传感器采集焊缝图像.通过调整滤、减光组合使得同一帧图像中出现稳定的电弧区域、坡口边缘线和缝隙接头线.设计有效算法直接提取缝隙接头线.以电弧区域的几何中心反馈焊枪位置，设计有效算法在缝隙接头线上确定焊枪当前跟踪位置，并借助视觉标定技术将其转换为世界坐标.利用用户软件系统从机器人控制系统中获取焊枪在世界坐标系下的坐标及转换后的坐标.结果表明，文中算法可以有效获取焊枪的三维偏差.
    Abstract: Three-dimensional (3D) deviation extraction of the welding torch for the automated welding process is a prerequisite in robotic intelligentized arc welding. In this paper, with the welding robot meal active gas (MAG) arc welding method was used to join steel sheets with micro grooves. Passive vision sensors with the appropriate combination of the filter and dimmer glass were used to capture images which contained complete arc regions, edge lines of the seam, and joint lines. An effective algorithm was then proposed to directly extract the seam line of the butt joint. To determine the tracking point at each sampling time the real-time position of the welding torch was first signed with the geometric center of the arc region. Then, another algorithm was suggested to determine the tracking point that lies in the extracted seam line. It was transformed into the world coordinate using existing vision calibrated techniques, and the 3D deviation was yielded when the exact position of the welding torch in the world coordinate system had been recorded from the control system of the welding robot. Experimental results show that the proposed method in this paper can effectively acquire the 3D deviation of the welding torch in real time.
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    • 参考文献(10)
  • [1] Xu Y, Lü N, Fang G, et al. Welding seam tracking in robotic gas metal arc welding[J]. Journal of Materials Processing Technology, 2017, 248:18-30.
    [2] Guo B, Shi Y, Yu G, et al. Weld deviation detection based on wide dynamic range vision sensor in MAG welding process[J]. International Journal of Advanced Manufacturing Technology, 2016, 87(9-12):3397-3410.
    [3] Xu Y, Fang G, Chen S, et al. Real-time image processing for vision-based weld seam tracking in robotic GMAW[J]. International Journal of Advanced Manufacturing Technology, 2014, 73(9-12):1413-1425.
    [4] Xu Y, Yu H, Zhong J, et al. Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor[J]. Journal of Materials Processing Technology, 2012, 212(8):1654-1662.
    [5] Yong Z, Jiang L, Yunhua L, et al. Welding deviation detection algorithm based on extremum of molten pool image contour[J]. Chinese Journal of Mechanical Engineering, 2016, 29(1):74-83.
    [6] Gao X, Mo L, You D, et al. Tight butt joint weld detection based on optical flow and particle filtering of magneto-optical imaging[J]. Mechanical Systems & Signal Processing, 2017, 96:16-30.
    [7] 欧志辉, 孙振国. 基于区域连通滤波的薄板焊缝跟踪图像处理算法[J]. 焊接, 2016(12):37-40 Ou Zhihui, Sun Zhenguo. An image processing algorithm based on regional connectivity filtering of sheet weld track[J]. Welding & Joining, 2016(12):37-40
    [8] 何银水, 陈华斌, 张华军, 等. 基于方向显著性的T形接头厚板机器人焊接焊缝轮廓的提取[C]//第二十次全国焊接学术会议论文集, 兰州, 甘肃, 机械工业出版社, 2015, 318– 323.
    [9] 何银水, 孔萌, 陈华斌, 等. 基于视觉注意机制的机器人厚板焊接焊缝轮廓的识别[J]. 焊接学报, 2015, 36(12):51-55 He Yinshui, Kong Meng, Chen Huabin, et al. Weld seam profile identification based on visual attention mechanism in robotic thick-plate welding[J]. Transactions of the China Welding Institution, 2015, 36(12):51-55
    [10] 何银水, 胡兆吉, 胡宗梅, 等. 改进的近邻聚类算法用于水下焊缝图像的识别[J]. 电焊机, 2013, 43(5):89-92 He Yinshui, Hu Zhaoji, Hu Zongmei, et al. An improved algorithm of close neighbor clustering was used on underwater weld recognition[J]. Electric Welding Machine, 2013, 43(5):89-92
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出版历程
  • 收稿日期:  2018-08-31

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