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MAO Zhiwei, ZHOU Shaoling, ZHAO Bin, SHI Zhixin, JIANG Yinsong, PAN Jiluan. Welding torch position and seam orientation deviation based on two stripes laser vision sensing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(2): 35-38.
Citation: MAO Zhiwei, ZHOU Shaoling, ZHAO Bin, SHI Zhixin, JIANG Yinsong, PAN Jiluan. Welding torch position and seam orientation deviation based on two stripes laser vision sensing[J]. TRANSACTIONS OF THE CHINA WELDING INSTITUTION, 2015, 36(2): 35-38.
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Welding torch position and seam orientation deviation based on two stripes laser vision sensing

  • 1.

    School of Mechanical and Electrical Engineering, Nanchang University, Nanchang 330031, China

  • 2.

    Jiangxi Industry Polytechnic College, Nanchang 330095, China

  • 3.

    Department of Mechanical Engineering, Tsinghua University, Beijing 100084, China

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  • Received Date: July 13, 2013
    Graphical Abstract
    • Abstract
  • To detect the welding torch position, recognize the seam orientation and improve the accuracy of seam tracking in robot welding, an intercrossing two stripes laser vision sensing was put forward, and a theoretical model of the welding torch height using the sensor was set up. Through extracting region of interest(ROI) in accordance with the image line derivative, making Otsu threshold segmentation and extracting Canny boundary, the distance between the up and down laser stripes and the central position of groove were obtained. The height of welding torch were obtained by the theoretical model of the welding torch height, and the weld accurate deviation and the welding seam trajectory direction was obtained by the up and down laser stripes and the central position of groove. The results show that, the theoretical model of welding torch height is correct, and the method can quickly realize the welding torch positioning and improve the accuracy of welding seam deviation recognition. Meanwhile it can also identify the welding seam trajectory direction, reduce prepositive errors and improve the intelligent level of welding robot.
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    • References (5)
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