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Welding deviation detection algorithm based on extremum of molten pool image contour

  • Advanced Manufacturing and Intelligence Materials
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Abstract

The welding deviation detection is the basis of robotic tracking welding, but the on-line real-time measurement of welding deviation is still not well solved by the existing methods. There is plenty of information in the gas metal arc welding(GMAW) molten pool images that is very important for the control of welding seam tracking. The physical meaning for the curvature extremum of molten pool contour is revealed by researching the molten pool images, that is, the deviation information points of welding wire center and the molten tip center are the maxima and the local maxima of the contour curvature, and the horizontal welding deviation is the position difference of these two extremum points. A new method of weld deviation detection is presented, including the process of preprocessing molten pool images, extracting and segmenting the contours, obtaining the contour extremum points, and calculating the welding deviation, etc. Extracting the contours is the premise, segmenting the contour lines is the foundation, and obtaining the contour extremum points is the key. The contour images can be extracted with the method of discrete dyadic wavelet transform, which is divided into two sub contours including welding wire and molten tip separately. The curvature value of each point of the two sub contour lines is calculated based on the approximate curvature formula of multi-points for plane curve, and the two points of the curvature extremum are the characteristics needed for the welding deviation calculation. The results of the tests and analyses show that the maximum error of the obtained on-line welding deviation is 2 pixels(0.16 mm), and the algorithm is stable enough to meet the requirements of the pipeline in real-time control at a speed of less than 500 mm/min. The method can be applied to the on-line automatic welding deviation detection.

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Author information

Authors and Affiliations

  1. College of Automation Science and Electrical Engineering, Beihang University, Beijing, 100191, China

    Yong Zou & Yunhua Li

  2. Optical Electromechanical Equipment Technology Key Laboratory of Beijing, Beijing Institute of Petrochemical Technology, Beijing, 102617, China

    Yong Zou, Lipei Jiang, Long Xue, Junfen Huang & Jiqiang Huang

Authors
  1. Yong Zou
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  2. Lipei Jiang
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  3. Yunhua Li
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  4. Long Xue
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  5. Junfen Huang
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  6. Jiqiang Huang
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Corresponding author

Correspondence to Yong Zou.

Additional information

ZOU Yong, born in 1976, is currently a PhD candidate at Beihang University, China. He is also a teacher at Beijing Institute of Petrochemical Technology, China. He received his master degree from China University of Petroleum(Beijing), China, in 2004. His research interests include welding automation and robots.

JIANG Lipei, born in 1942, is currently a professor and a PhD candidate supervisor at Beijing Institute of Petrochemical Technology, China. His research interests include welding power and technology, welding automation and underwater welding.

LI Yunhua, born in 1963, is currently a professor and a PhD candidate supervisor at Beihang University, China. His main research interests include mechachonics, hydraulic control, and robotics.

XUE Long, born in 1966, is currently a professor at Beijing Institute of Petrochemical Technology, China. His research interests include automation and robotics, underwater welding.

HUANG Junfen, born in 1975, is currently a lecturer at Beijing Institute of Petrochemical Technology, China. Her research interests include underwater welding, robotics.

HUANG Jiqiang, born in 1971, is currently an associate professor at Beijing Institute of Petrochemical Technology, China. His research interests include underwater welding, automation.

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Zou, Y., Jiang, L., Li, Y. et al. Welding deviation detection algorithm based on extremum of molten pool image contour. Chin. J. Mech. Eng. 29, 74–83 (2016). https://doi.org/10.3901/CJME.2015.0908.110

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  • DOI: https://doi.org/10.3901/CJME.2015.0908.110

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