Skip to main content
Log in

An adaptive welding method for grooves with position and size errors

  • Research Paper
  • Published:
Welding in the World Aims and scope Submit manuscript

Abstract

A large batch of mass-produced components, represented by architectural steel structure joint spheres, is influenced by the upstream processing technology level, making it difficult to maintain the position and dimensions of the groove assembly within the standard size range. This results in the inability to use the same process parameters and welding strategy for all weld joints during welding. For components with significant groove size errors, a U-shaped residual network (U-ResNet) was established and employed to segment laser stripe images at the grooves and effectively extract feature data. Subsequently, based on the groove’s position and size information, an established prediction model was used to adaptively output process parameters. Finally, validation experiments were conducted on actual components. The range of variation in the height dimension of the joint sphere’s circumferential weld seam was within 2 mm, with no unfused defects. The entire welding process took 20 min, meeting the requirements for industrial on-site production.

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

Access this article

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

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

Data Availability

Data available on request from the authors.

References

  1. Xu Y, Wang Z (2021) Visual sensing technologies in robotic welding: recent research developments and future interests. Sens Actuators, A 320:112551. https://doi.org/10.1016/j.sna.2021.112551

    Article  CAS  Google Scholar 

  2. Lei T, Huang Y, Shao W, Liu W, Rong Y (2020) A tactual weld seam tracking method in super narrow gap of thick plates. Robot Comput-Integr Manuf 62:101864. https://doi.org/10.1016/j.rcim.2019.101864

    Article  Google Scholar 

  3. Baek D, Moon HS, Park SH (2017) Development of an automatic orbital welding system with robust weaving width control and a seam-tracking function for narrow grooves. Int J Adv Manuf Technol 93(1):767–777. https://doi.org/10.1007/s00170-017-0562-0

    Article  Google Scholar 

  4. Wenji L, Zhenyu G, Xiao J, Li L, Jianfeng Y (2019) Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing. Sens Actuators, A 292:205–216. https://doi.org/10.1016/j.sna.2019.04.015

    Article  CAS  Google Scholar 

  5. Zhu J, Wang J, Su N, Xu G, Yang M (2017) An infrared visual sensing detection approach for swing arc narrow gap weld deviation. J Mater Process Technol 243:258–268. https://doi.org/10.1016/j.jmatprotec.2016.12.029

    Article  Google Scholar 

  6. Wu K, Wang T, He J, Liu Y, Jia Z (2020) Autonomous seam recognition and feature extraction for multi-pass welding based on laser stripe edge guidance network. Int J Adv Manuf Technol 111(9):2719–2731. https://doi.org/10.1007/s00170-020-06246-1

    Article  Google Scholar 

  7. Chen S, Liu J, Chen B, Suo X (2022) Universal fillet weld joint recognition and positioning for robot welding using structured light. Robot Comput-Integr Manuf 74:102279. https://doi.org/10.1016/j.rcim.2021.102279

    Article  Google Scholar 

  8. Muhammad J, Altun H, Abo-Serie E (2018) A robust butt welding seam finding technique for intelligent robotic welding system using active laser vision. Int J Adv Manuf Technol 94(1):13–29. https://doi.org/10.1007/s00170-016-9481-8

    Article  Google Scholar 

  9. Wang N, Zhong K, Shi X, Zhang X (2020) A robust weld seam recognition method under heavy noise based on structured-light vision. Robot Comput-Integr Manuf 61:101821. https://doi.org/10.1016/j.rcim.2019.101821

    Article  Google Scholar 

  10. Xiao R, Xu Y, Hou Z, Chen C, Chen S (2019) An adaptive feature extraction algorithm for multiple typical seam tracking based on vision sensor in robotic arc welding. Sens Actuators, A 297:111533. https://doi.org/10.1016/j.sna.2019.111533

    Article  CAS  Google Scholar 

  11. Zou Y, Chen T, Chen X, Li J (2022) Robotic seam tracking system combining convolution filter and deep reinforcement learning. Mech Syst Signal Process 165:108372. https://doi.org/10.1016/j.ymssp.2021.108372

    Article  Google Scholar 

  12. Xu F, Zhang H, Xiao R, Hou Z, Chen S (2022) Autonomous weld seam tracking under strong noise based on feature-supervised tracker-driven generative adversarial network. J Manuf Process 74:151–167. https://doi.org/10.1016/j.jmapro.2021.12.004

    Article  Google Scholar 

  13. Fabry C, Pittner A, Rethmeier M (2018) Design of neural network arc sensor for gap width detection in automated narrow gap GMAW. Weld World 62(4):819–830. https://doi.org/10.1007/s40194-018-0584-8

    Article  Google Scholar 

  14. Wang W, Shi Y, Li C, Gu Y (2023) Feature information extraction method for narrow gap U-type groove based on laser vision. 104:257–272. https://doi.org/10.1016/j.jmapro.2023.08.053

  15. Yu R, Kershaw J, Wang P, Zhang YM (2021) Real-time recognition of arc weld pool using image segmentation network. 72:159–167. https://doi.org/10.1016/j.jmapro.2021.10.019

Download references

Funding

This work was supported by Excellent Doctoral Program of Natural Science Foundation of Gansu Province of China (22JR5RA239), National Natural Science Foundation of China (#52005237), Major Science and Technology Project of Gansu Province of China (22ZD6GA008), and Zhejiang Provincial Natural Science Foundation of China under Grant (LQ21E050023).

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Yu Shi.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Recommended for publication by Commission XII - Arc Welding Processes and Production Systems.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wang, W., Shi, Y., Li, C. et al. An adaptive welding method for grooves with position and size errors. Weld World 68, 755–764 (2024). https://doi.org/10.1007/s40194-023-01629-w

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40194-023-01629-w

Keywords

Navigation