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Groove-center detection in gas metal arc welding using a template-matching method

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Abstract

To achieve welding automation, the center of the groove needs to be detected accurately during welding. We developed a method based on template-matching to detect the groove center during gas metal arc welding (GMAW). To avoid the negative influence of the strong GMAW arc light, a high-dynamic-range camera was used to capture details of the welding arc, molten pool, and the V-groove simultaneously in a single image. Two image-processing and object-detection algorithms were developed to detect the center of the welding pool and the groove based on template matching. The experimental results of the latter algorithm were more accurate for identifying the position of the groove center. However, interference in the welding process caused the template-matching method to fail under certain conditions. Therefore, the two detection algorithms were combined to improve the detection accuracy. After filtration of the detected welding-pool center, the groove-center detection algorithm based on template matching results in higher accuracy.

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References

  1. Lee SK, Na SJ (2002) A study on automatic seam tracking in pulsed laser edge welding by using a vision sensor without an auxiliary light source. J Manuf Syst 21(4):302–315

    Article  Google Scholar 

  2. Chen SB, Zhang Y, Qiu T, Lin T (2003) Robotic welding systems with vision sensing and self-learning neuron control of Arc weld dynamic process. J Intell Robot Syst 36:191–208

    Article  Google Scholar 

  3. Ge J, Zhu Z, He D, Chen L (2005) A vision-based algorithm for seam detection in a PAW process for large-diameter stainless steel pipes. Int J Adv Manuf Technol 26(10):1006–1011

    Article  Google Scholar 

  4. Kawahara M (1983) Tracking control system using image sensor for arc welding. Automatica 19:357–363

    Article  Google Scholar 

  5. Smith JS, Lucas J (1989) A vision-based seam tracker for butt-plate TIG welding. J Phys, E Sci Instrum 22(9):739–744

    Article  Google Scholar 

  6. Smith JS, Lucas J (1989) A vision-based seam tracker for butt-plate TIG welding. J Phys E: Sci Inst 22(9):739

    Article  Google Scholar 

  7. Yu JY, Na SJ (1997) A study on vision sensors for seam tracking of height-varying weldment, part 1: mathematical model. Mechatronics 7(7):599–612

    Article  Google Scholar 

  8. Yu JY, Na SJ (1998) A study on vision sensors for seam tracking of height-varying weldment, part 2: applications. Mechatronics 8(1):21–36

    Article  Google Scholar 

  9. Xu P, Xu G, Tang X, Yao S (2007) A visual seam tracking system for robotic arc welding. Int J Adv Manuf Technol 37(1–2):70–75

    Google Scholar 

  10. Chen Z, Gao X (2014) Detection of weld pool width using infrared imaging during high-power fiber laser welding of type 304 austenitic stainless steel. Int J Adv Manuf Technol 74:1247–1254

    Article  Google Scholar 

  11. Xu D, Jiang Z, Wang L, Tan M (2004) Features extraction for structured light image of welding seam with arc and splash disturbance. In: Proceedings 8th IEEE Control, Automation, Robotics and Vision Conf., vol 3, pp 1559–1563

  12. Shen HY, Wu J, Lin T, Chen SB (2008) Arc welding robot system with seam tracking and weld pool control based on passive vision. Int J Adv Manuf Technol 39:669–678

    Article  Google Scholar 

  13. Nele L, Sarno E, Keshari A (2013) An image acquisition system for real-time seam tracking. Int J Adv Manuf Technol 69:2099–2110

    Article  Google Scholar 

  14. Xu Y, Fang G, Chen S, Zou JJ, Ye Z (2014) Real-time image processing for vision-based weld seam tracking in robotic GMAW. Int J Adv Manuf Technol 73:1413–1425

    Article  Google Scholar 

  15. Otsu N (1979) A threshold selection method from gray level histograms. IEEE Trans on SMC 9(1):62–69

    MathSciNet  Google Scholar 

  16. Yin SF, Wang YC, Cao LC, Jin GF, Ling YS (2010) Fast correlation matching based on fast fourier transform and integral image. Acta Photonica Sin 39(12):2246–2249

    Article  Google Scholar 

  17. Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings IEEE Conference on Computer Vision and Pattern Recognition

Download references

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Authors and Affiliations

  1. School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, Guangdong, 510640, People’s Republic of China

    Xiaojia Wang, Yonghua Shi & Yang Li

  2. Guangzhou Bureau, Navy Armament Department of China, Guangzhou, Guangdong, 510300, People’s Republic of China

    Guoqing Yu & Bin Liang

Authors
  1. Xiaojia Wang
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  2. Yonghua Shi
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  3. Guoqing Yu
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  4. Bin Liang
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  5. Yang Li
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Corresponding author

Correspondence to Yonghua Shi.

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Wang, X., Shi, Y., Yu, G. et al. Groove-center detection in gas metal arc welding using a template-matching method. Int J Adv Manuf Technol 86, 2791–2801 (2016). https://doi.org/10.1007/s00170-016-8389-7

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  • DOI: https://doi.org/10.1007/s00170-016-8389-7

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