Skip to main content
Springer Nature Link
Log in

Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring

  • Published:
Journal of Intelligent and Robotic Systems Aims and scope Submit manuscript

Abstract

The real-time detection of the state of the gap and weld penetration control are two fundamental issues in robotic arc welding. However, traditional robotic arc welding lacks external information feedback and the function of real-time adjusting. The objective of this research is to adopt new sensing techniques and artificial intelligence to ensure the stability of the welding process through controlling penetration depth and weld pool geometry. A novel arc welding robot system including function modules (visual modules, data acquisition modules) and corresponding software system was developed. Thus, the autonomy and intelligence of the arc welding robot system is realized. Aimed at solving welding penetration depth, a neural network (NN) model is developed to calculate the full penetration state, which is specified by the back-side bead width (Wb), from the top-side vision sensing technique. And then, a versatile algorithm developed to provide robust real-time processing of images for use with a vision-based computer control system is discussed. To this end, the peak current self adaptive regulating controller with weld gap compensation was designed in the robotic arc welding control system. Using this closed-loop control experiments have been conducted to verify the effectiveness of the proposed control system for the robotic arc welding process. The results show that the standard error of the Wb is 0.124 regardless of the variations in the state of the gap.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

Explore related subjects

Discover the latest articles, news and stories from top researchers in related subjects.

References

  1. Tsai, C.-H., et al.: Fuzzy control of pulsed GTA welds by using real-time root bead image feedback. J. Mater. Process. Technol. 176, 158–167 (2006). doi:10.1016/j.jmatprotec.2006.02.027

    Article  Google Scholar 

  2. Luo, H.: Robotic welding, intelligence and automation, laser visual sensing and process control in robotic arc welding of titanium alloys. LNCIS 299, 110–122 (2004)

    Google Scholar 

  3. Valavanis, K.P., Saridis, G.N.: Intelligent Robotics System: Theory, Design and Applications, pp. 12–18. Boston (1992)

  4. Miller, M., Mi, B., Kita, A., Ume, C.: Development of automated real-time data acquisition system for robotic weld quality monitoring. Mechatronics 12, 1259–1269 (2002). doi:10.1016/S0957-4158(02)00028-4

    Article  Google Scholar 

  5. Song, H.S., Zhang, Y.M.: Measurement and analysis of three-dimensional specular gas tungsten arc weld pool surface. Weld. J. 87(4), 85–95s (2008)

    Google Scholar 

  6. Adolfsson, S., Bahrami, A., Bolomsjo, G., Cleason, I.: On-line quality monitoring in short-circuit gas metal arc welding. Weld. J. 78(2), 59–73s (1999)

    Google Scholar 

  7. Casalino, G., Hu, S.J., Hou, W.: Deformation prediction and quality evaluation of the gas metal arc welding butt weld. Proc. Inst. Mech. Eng., B J. Eng. Manuf. 217(11), 1615–1622 (2003)

    Article  Google Scholar 

  8. Kovacevic, R., Zhang, Y.M.: Real-time image processing for monitoring of free weld pool surface. J. Manuf. Sci. Eng. 119(5), 161–169 (1997). doi:10.1115/1.2831091

    Article  Google Scholar 

  9. Kovacevic, R., Zhang, Y.M., Li, L.: Monitoring of weld penetration based on weld pool geometrical appearance. Weld. J. 75(10), 317–328 (1996)

    Google Scholar 

  10. Lim, T.G., Cho, H.S.: Estimation of weld pool sizes in GMA welding process using neural networks. J. Syst. Control Eng. 207(1), 15–26 (1993)

    Google Scholar 

  11. Saeed, G., Zhang, Y.M., Jaynes, C.: Weld pool surface monitoring and depth extraction using a calibrated CCD sensor. In: ASM Proceedings of the International Conference: Trends in Welding Research, vol. 2005, pp. 665–670 (2005)

  12. Bae, K.Y., Lee, T.H., Ahn, K.C.: An optical sensing system for seam tracking and weld pool control in gas metal arc welding of steel pipe. J. Mater. Process. Technol. 120(2), 458–465 (2002). doi:10.1016/S0924-0136(01)01216-X

    Article  Google Scholar 

  13. Chen, S.B., Wu, L., Wang, Q.L.: Self-learning fuzzy neural networks for control of uncertain systems with time delay. IEEE Trans. Syst. Man Cybern., Part B, Cybern. 27(1), 142–148 (1997)

    Article  Google Scholar 

  14. Chen, S.B., Chen, X.Z., Qiu, T., Li, J.Q.: Acquisition of weld seam dimensional position information for arc welding robot based on vision computing. J. Intell. Robot. Syst. 43, 77–97 (2005). doi:10.1007/s10846-005-2966-6

    Article  Google Scholar 

  15. Akira, H., Yasuyoshi, K., et al.: Sensing and control of weld pool by fuzzy-neural network in robotic welding system. IECON’01.2001:238–242

  16. Balfour, C., Smith, J.S, AI-Shamma, A.I.: A novel edge feature correlation algorithm for real-time computer vision-based molten weld pool measurements. Weld. J. 85(1), 1–8s (2006)

    Google Scholar 

  17. Kristinn, E., Cook, G.E.: Artificial neural networks applied to arc welding process modeling and control. IEEE Trans. Ind. Appl. 26(5), 824–830 (1990). doi:10.1109/28.60056

    Article  Google Scholar 

  18. Juang, S.C., Tarng, Y.S.: A comparison between the back-propagation and counter-propagation networks in the modeling of the TIG welding process. J. Mater. Process. Technol. 75, 54–62 (1998) doi:10.1016/S0924-0136(97)00292-6

    Article  Google Scholar 

  19. Mi, B.: Implementation of fiber phased array ultrasound generation system and signal analysis for weld penetration control. Doctoral Dissertation Georgia Institute of Technology, pp. 9–45, (2003)

  20. Meer, P., Georgescu, B.: Edge detection with embedded confidence. IEEE Trans. PAMI 23(120), 1351–1365 (2001)

    Google Scholar 

  21. Song, P., Zuxiang, F.: The joint optimization of BP learning algorithm. J. Circuits Syst. 5(3), 26–30 (2000)

    Google Scholar 

  22. Wang, J.J., Lin, T., Chen, S.: Obtaining of weld pool vision information during aluminum alloy TIG Welding. Int. J. Adv. Manuf. Technol. 26, 219–227 (2005). doi:10.1007/s00170-003-1548-7

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Welding Engineering Institute of Material Science and Engineering, Shanghai Jiao Tong University, Shanghai, China

    Huabin Chen, Fenglin Lv, Tao Lin & Shanben Chen

Authors
  1. Huabin Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Fenglin Lv
    View author publications

    You can also search for this author inPubMed Google Scholar

  3. Tao Lin
    View author publications

    You can also search for this author inPubMed Google Scholar

  4. Shanben Chen
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Huabin Chen.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Chen, H., Lv, F., Lin, T. et al. Closed-Loop Control of Robotic Arc Welding System with Full-penetration Monitoring. J Intell Robot Syst 56, 565 (2009). https://doi.org/10.1007/s10846-009-9329-7

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s10846-009-9329-7

Keywords