Control of weld penetration depth using relative fluctuation coefficient as feedback

  • Shuangyang Zou
  • Zhijiang WangEmail author
  • Shengsun Hu
  • Wandong Wang
  • Yue Cao


The monitoring and control of weld penetration in pulsed gas metal arc welding (GMAW-P) is considerably challenging, especially in field applications. The metal transfer and pulse current in GMAW-P complicate the identification of weld penetration. In previous studies, the authors found that both the change in arc voltage during the peak current period and the average arc voltage during the peak current period can be used for condition monitoring of weld pool surface and thus for the estimation of GMAW-P penetration depth. In the present work, the relative fluctuation coefficient (CRF) of weld pool surface is proposed by combining these two signals to predict the weld penetration depth. Model predictive control using this coefficient as feedback is employed to control the penetration depth. The experimental results show that uniform weld penetration depth can be obtained by the adaptive control algorithm. The practice attempted in this work can be expected to be a candidate solution for GMAW-P penetration control, which is easy to implement in field applications.


Arc sensing Weld pool surface Nonlinear model Predictive control Weld penetration Penetration depth GMAW-P 



This study is supported by National Natural Science Foundation of China (Grant No. 51505326), Natural Science Foundation of Tianjin (Grant No. 16JCQNJC04300) and the Regional Demonstration Project of Marine Economic Innovation and Development (Grant No. BHSF2017-10).


  1. Carlson, N. M., & Johnson, J. A. (1988). Ultrasonic sensing of weld pool penetration. Welding Journal, 67(11), 239s–246s.Google Scholar
  2. Chen, Z. Y., Chen, J., & Feng, Z. L. (2017). Monitoring weld pool surface and penetration using reversed electrode images. Welding Journal, 96(10), 367s–375s.Google Scholar
  3. Chen, J. S., Chen, J., Zhang, K., Feng, Z., & Zhang, Y. M. (2018). Dynamic reflection behaviors of weld pool surface in pulsed GTAW. Welding Journal, 97(6), 191s–206s.CrossRefGoogle Scholar
  4. Cheng, Y. C., Xiao, J., Chen, S. J., & Zhang, Y. M. (2018). Intelligent penetration welding of thin-plate GTAW process based on arc voltage feedback. Transactions of the China Welding Institution, 39(12), 1–4.Google Scholar
  5. Chokkalingham, S., Chandrasekhar, N., & Vasudevan, M. (2012). Predicting weld bead width and depth of penetration from infrared thermal image of weld pool using artificial neural network. Journal of Intelligent Manufacturing, 23(5), 1995–2001.CrossRefGoogle Scholar
  6. Fan, C. J., Lv, F. L., & Chen, S. B. (2009). Visual sensing and penetration control in aluminum alloy pulsed GTA welding. International Journal of Advanced Manufacturing Technology, 42(1–2), 126–137.CrossRefGoogle Scholar
  7. Hu, J., Guo, H., & Tsai, H. L. (2008). Weld pool dynamics and the formation of ripples in 3D gas metal arc welding. International Journal of Heat and Mass Transfer, 51(9–10), 2537–2552.CrossRefGoogle Scholar
  8. Li, C. K., Shi, Y., Gu, Y., & Yuan, P. (2018). Monitoring weld pool oscillation using reflected laser pattern in gas tungsten arc welding. Journal of Materials Processing Technology, 255, 876–885.CrossRefGoogle Scholar
  9. Liang, Z. M., Chang, H. X., Wang, Q. Y., Wang, D. L., & Zhang, Y. M. (2019). 3D reconstruction of weld pool surface in pulsed GMAW by passive biprism stereo vision. IEEE Robotics and Automation Letters, 4(3), 3091–3097.CrossRefGoogle Scholar
  10. Liu, Y. K., & Zhang, Y. M. (2014). Model-based predictive control of weld penetration in gas tungsten arc welding. IEEE Transactions on Control Systems Technology, 22(3), 955–966.CrossRefGoogle Scholar
  11. Ma, X. J., & Zhang, Y. M. (2009). Reflection of illumination laser from gas metal arc weld pool surface. Measurement Science & Technology, 20(11), 115105.CrossRefGoogle Scholar
  12. Ma, X. J., & Zhang, Y. M. (2011). Gas metal arc weld pool surface imaging: modeling and processing. Welding Journal, 90(5), 85s–94s.Google Scholar
  13. Mnich, C., Al-Bayat, F., Debrunner, C., Steele, J., & Vincent, T. (2004). In situ weld pool measurement using stereovision. In Proceedings of 2004 JapanUSA symposium on flexible automation (pp. 1–2), ASME.Google Scholar
  14. Modi, S., Lin, Y. Z., Cheng, L., Yang, G. S., Liu, L. Z., & Zhang, W. J. (2011). A socially inspired framework for human state inference using expert opinion integration. IEEE/ASME Transactions on Mechatronics, 16(5), 874–878.CrossRefGoogle Scholar
  15. Rao, Z. H., Zhou, J., Liao, S. M., & Tsai, H. L. (2010). Three-dimensional modeling of transport phenomena and their effect on the formation of ripples in gas metal arc welding. Journal of Applied Physics, 107(5), 054905.CrossRefGoogle Scholar
  16. Renwick, R. J., & Richardson, R. W. (1983). Experimental investigation of GTA weld pool oscillations. Welding Journal, 62(2), 29s–35s.Google Scholar
  17. Rokhlin, S. I., & Guu, A. C. (1990). Computerized radiographic sensing and control of an arc welding process. Welding Journal, 69(3), 83s–95s.Google Scholar
  18. Shi, Y., Li, C. K., Du, L. M., Gu, Y. F., & Zhu, M. (2016). Frequency characteristics of weld pool oscillation in pulsed gas tungsten arc welding. Journal of Manufacturing Processes, 24, 145–151.CrossRefGoogle Scholar
  19. Song, H. S., & Zhang, Y. M. (2008). Measurement and analysis of three-dimensional specular gas tungsten arc weld pool surface. Welding Journal, 87(4), 85s–95s.Google Scholar
  20. Wang, Z. Z. (2014). Monitoring of GMAW weld pool from the reflected laser lines for real-time control. IEEE Transactions on Industrial Informatics, 10(4), 2073–2083.CrossRefGoogle Scholar
  21. Wang, Z. Z. (2015). An imaging and measurement system for robust reconstruction of weld pool during arc welding. IEEE Transactions on Industrial Electronics, 62(8), 5109–5118.CrossRefGoogle Scholar
  22. Wang, Z. Z. (2017). Unsupervised recognition and characterization of the reflected laser lines for robotic gas metal arc welding. IEEE Transactions on Industrial Informatics, 13(4), 1866–1876.CrossRefGoogle Scholar
  23. Wang, J. F., Wang, W. Y., & Chen, S. B. (2009). Inspection of welding pool height from shading in pulsed GTAW with wire filler. Industrial Robot, 36(3), 270–276.CrossRefGoogle Scholar
  24. Wang, W. D., Wang, Z. J., Hu, S. S., Bai, P., Lu, T., & Cao, Y. (2018). Weld pool surface fluctuations sensing in pulsed GMAW. Welding Journal, 97(12), 327s–337s.CrossRefGoogle Scholar
  25. Wang, Q. L., Yang, C. L., & Geng, Z. (1993). Separately excited resonance phenomenon of the weld pool and its application. Welding Journal, 72(9), 455s–462s.Google Scholar
  26. Wang, Z. J., Zhang, Y. M., & Wu, L. (2010). Measurement and estimation of weld pool surface depth and weld penetration in pulsed gas metal arc welding. Welding Journal, 89(6), 117s–126s.Google Scholar
  27. Xiao, Y. H., & Den Ouden, G. (1993). Weld pool oscillation during GTA welding of mild steel. Welding Journal, 72(8), 428s–434s.Google Scholar
  28. Yoo, C. D., & Richardson, R. W. (1993). An experimental study on sensitivity and signal characteristics of welds pool oscillation. Transactions of the Japan Welding Society, 24(2), 54–62.Google Scholar
  29. Zhang, W. J., Wang, X. W., & Zhang, Y. M. (2013a). Analytical real-time measurement of a three-dimensional weld pool surface. Measurement Science & Technology, 24(11), 115011.CrossRefGoogle Scholar
  30. Zhang, G. J., Yan, Z. H., & Wu, L. (2006). Reconstructing a three-dimensional P-GMAW weld pool shape from a two-dimensional visual image. Measurement Science & Technology, 17(7), 1877–1882.CrossRefGoogle Scholar
  31. Zhang, K., Zhang, Y. M., Chen, J. S., & Wu, S. J. (2017). Observation and analysis of three-dimensional weld pool oscillation dynamic behaviors. Welding Journal, 96(5), 143s–153s.Google Scholar
  32. Zhang, W. J., Zhang, X., & Zhang, Y. M. (2013b). Robust pattern recognition for measurement of three dimensional weld pool surface in GTAW. Journal of Intelligent Manufacturing, 26(4), 659–676.CrossRefGoogle Scholar
  33. Zhao, D. B., Yi, J. Q., Chen, S. B., Wu, L., & Chen, Q. (2003). Extraction of three-dimensional parameters for weld pool surface in pulsed GTAW with wire filler. Journal of Manufacturing Science and Engineering-Transactions of the ASME, 125(3), 493–503.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Tianjin Key Laboratory of Advanced Joining Technology, School of Materials Science and EngineeringTianjin UniversityTianjinChina

Personalised recommendations