Skip to main content

Advertisement

SpringerLink
Log in

Simultaneously extract 3D seam curve and weld head angle for robot arm using passive vision

  • Original Paper
  • Published:
International Journal on Interactive Design and Manufacturing (IJIDeM) Aims and scope Submit manuscript

Abstract

Welding automation is one of the highly applied research that receives a lot of interest from scientists as well as application engineers in the industry. The welding task can be tedious for the workers; health affected by chemical smoke; or activity performed in a human restricted area. However, at present, manual teaching using Tech-pendant and off-line programming are still used in most industrial applications especially 3D complex curve welding. In order to improve the quality and quantity of the welding task, this research presents an effective automated method for extracting 3D curve seam welding using industrial robot. Firstly, by using a low cost 3D Camera and laser system which are attached at the end-effector of 6-DOF manipulator and combining with laser triangular image processing algorithm the position of path from start, mid, auxiliary, and end points coordinated of the welding line are recorded. Then by using interpolation algorithm the trajectory is calculated and transmitted to the 6DOF manipulator. As known that the angle of the weld head is also very important to the quality of the weld, so this research also proposes the method to adjust this angle during welding. Through implement experiments, the system proved that it is stable and have good precision. Installation time and maintaining are fast, not complicated and meet the automation demand.

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

Access this article

Log in via an institution

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

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15

Similar content being viewed by others

Availability of data and materials

Not applicable.

References

  1. Ma, H., Wei, S., Sheng, Z., Lin, T., Chen, S.: Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding. Int. J. Adv. Manuf. Technol. 48, 945–953 (2010). https://doi.org/10.1007/s00170-009-2349-4

    Article  Google Scholar 

  2. Automation world, Yaskawa Motoman: Teaching Technology for Welding Robots, https://www.automationworld.com/factory/robotics/product/13310598/yaskawa-motoman-teaching-technology-for-welding-robots

  3. Rout, A., Deepak, B.B.V.L., Biswal, B.B.: Advances in weld seam tracking techniques for robotic welding: a review. Robot. Computer-Integrated Manuf 56, 12–37 (2019)

    Article  Google Scholar 

  4. Liu, Y., Tang, Q., Tian, X.: A discrete method of sphere-pipe intersecting curve for robot welding by offline programming. Robot. Computer-Integrated Manuf. 57, 404–411 (2019)

    Article  Google Scholar 

  5. ABB, Weldguide IV, The most powerful robotic Thru-the-Arc joint tracking on the market, https://new.abb.com/products/robotics/application-equipment-and-accessories/arc-welding-equipment/process-support-tools/weldguide

  6. Cook, G. E., Andersen, K., Fernandez, K. R., Shepard, M. E., Wells, A. M. Jr.: “Electric arc sensing for robot positioning control”, IFS(Publications) Ltd., Robotic Welding, 181–216 (2010)

  7. Fridenfalk, M., Bolmsjo, G.: Design and validation of a universal 6D seam-tracking system in robotic welding using arc sensing. Adv Robot 18, 1–21 (2004)

    Article  Google Scholar 

  8. Arc Sensor by Daihen, USA, (https://www.daihen-usa.com/product/fd-tr-tig-arcsensor/). Accessed: 2018–06–26.

  9. Suwanratchatamanee, K., Matsumoto, M., Hashimoto, S.: Robotic tactile sensor system and applications. IEEE Trans. Industr. Electron. 57(3), 1074–1087 (2010)

    Article  Google Scholar 

  10. Xu, Y., Lv, N., Zhong, J., Chen, H., Chen, S.: Research on the real-time tracking information of three-dimension welding seam in robotic GTAW process based on composite sensor technology. J. Intell. Rob. Syst. 68(2), 89–103 (2012)

    Article  Google Scholar 

  11. Silvers, G.A., Fang, G.: Human machine interface in weld seam detection using an RGB-D camera. Adv. Mater. Res. 875–877, 1967–1971 (2014)

    Article  Google Scholar 

  12. Wang, Q., Jiao, W., Yu, R., Johnson, M.T., Zhang, Y.M.: Virtual reality robot-assisted welding based on human intention recognition. IEEE Trans. Autom. Sci. Eng. 17(2), 799–808 (2020). https://doi.org/10.1109/TASE.2019.2945607

    Article  Google Scholar 

  13. Pablo, R.G., Manuel, R.M.: Weld bead detection based on 3D geometric featuresand machine learning approaches. IEEE Access 7, 14714–14727 (2019). https://doi.org/10.1109/ACCESS.2019.2891367

    Article  Google Scholar 

  14. Panasonic Industry, VRPS (Virtual Robot Programming System), https://industrial.panasonic.com/ww/products-fa/welding/industrial-robots/arc-welding-robot/vrps

  15. Kiddee, P., Fang, Z., Tan, M.: An automated weld seam tracking system for thick plate using cross mark structured light. Int. J. Adv. Manuf. Technol. 87, 3589–3603 (2016). https://doi.org/10.1007/s00170-016-8729-7

    Article  Google Scholar 

  16. Huang, Y., Li, G., Shao, W., Gong, S., Zhang, X.: A novel dual-channel weld seam tracking system for aircraft T-joint welds. Int. J. Adv. Manuf. Technol. 91, 751–761 (2017)

    Article  Google Scholar 

  17. Zou, Y., Li, Y., Jiang, L., Xue, L.: Weld pool image processing algorithm for seam tracking of welding robot. In: 6th IEEE Conference on Industrial Electronics and Applications, Beijing, China, Jun. 2011, pp. 161–165

  18. Xu, Y., Fang, G., Lv, N., Chen, S., Zou, J.J.: Computer vision technology for seam tracking in robotic GTAW and GMAW. Robot. Computer-Integrated Manuf. 32, 25–36 (2015)

    Article  Google Scholar 

  19. Dinham, M., Fang, G.: Autonomous weld seam identification and localisation using eye-in-hand stereo vision for robotic arc welding. Robot. Computer-Integrated Manuf. 29(5), 288–301 (2013)

    Article  Google Scholar 

  20. Zhou, L., Lin, T., Chen, S.-B.: Autonomous acquisition of seam coordinates for arc welding robot based on visual servoing. J. Intell. Rob. Syst. 47(3), 239–255 (2006)

    Article  Google Scholar 

  21. Shen, H., Lin, T., Chen, S., Li, L.: Real-time seam tracking technology of welding robot with visual sensing. J. Intell. Rob. Syst. 59(3–4), 283–298 (2010)

    Article  Google Scholar 

  22. Shen, H.Y., Wu, J., Lin, T., Chen, S.B.: Arc welding robot with seam tracking and weld pool control based on passive vision. J. Adv. Manuf. Technol. 39(7–8), 669–678 (2008)

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. Liu, J., Fan, Z., Olsen, S.I., Christensen, K.H., Kristensen, J.K.: Boosting active contours for weld pool visual tracking in automatic arc welding. IEEE Trans. Autom. Sci. Eng. 14(2), 1096–1108 (2017)

    Article  Google Scholar 

  25. Xu, Y., Yu, H., Zhong, J., Lin, T., Chen, S.: Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor. J. Mater. Process. Technol. 212(8), 1654–1662 (2012)

    Article  Google Scholar 

  26. Xiong, J., Pi, Y., Chen, H.: Deposition height detection and feature point extrac-tion in robotic GTA-based additive manufacturing using passive vision sensing. Robot. Computer-Integrated Manuf. 59, 326–334 (2019). https://doi.org/10.1016/j.rcim.2019.05.006

    Article  Google Scholar 

  27. Li, J., Yang, P., Huang, L., Xiao, M.: Recognition of initial welding position forlarge diameter pipeline based on pulse coupled neural network. Ind. Robot. 42, 339–346 (2015). https://doi.org/10.1108/IR-01-2015-0011

    Article  Google Scholar 

  28. Jin, Z., Li, H., Zhang, C., Wang, Q., Gao, H.: Online welding path detection in auto-matic tube-to-tubesheet welding using passive vision. Int. J. Adv. Manuf. Technol. 90, 3075–3084 (2017). https://doi.org/10.1007/s00170-016-9649-2

    Article  Google Scholar 

  29. Yang, L., Li, E., Long, T., Fan, J., Liang, Z.: A high-speed seam extraction method based on the novel structured-light sensor for arc welding robot: a review. IEEE Sens. J. 18(21), 8631–8641 (2018)

    Article  Google Scholar 

  30. Du, J., Yong, L.Y., Sun, M., Ge, J.S.: Study on stereo vision for 3D reconstruction of welding seam. Adv. Mater. Res. 850–851, 212–216 (2013)

    Article  Google Scholar 

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

    Article  Google Scholar 

  32. Fan, J., Jing, F., Yang, L., Teng, L., Tan, M.: A precise initial weld point guidingmethod of micro-gap weld based on structured light vision sensor. IEEE Sens. J. 19, 322–331 (2019). https://doi.org/10.1109/JSEN.2018.2876144

    Article  Google Scholar 

  33. Du, R.Q., Xu, Y., Hou, Z., Shu, J., Chen, S.: Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding. Int. J. Adv. Manuf. Technol. 101, 2135–2149 (2019)

    Article  Google Scholar 

  34. Li, Y., Hu, M., Taiyong, W.: Weld image recognition algorithm based on deep learning. Int. J. Pattern Recognit. Artif. Intell. 34(08), 2052004 (2020)

    Article  Google Scholar 

  35. Rodríguez-Gonzálvez, P., Rodríguez-Martín, M.: Weld bead detection based on 3D geometric features and machine learning approaches. IEEE Access 7, 14714–14727 (2019). https://doi.org/10.1109/ACCESS.2019.2891367

    Article  Google Scholar 

  36. Yang, L., Liu, Y., Peng, J.: An automatic detection and identification method of welded joints based on deep neural network. IEEE Access 7, 164952–164961 (2019). https://doi.org/10.1109/ACCESS.2019.2953313

    Article  Google Scholar 

  37. Hanh, L.D., Tu, H.B.: Computer vision for industrial robot in planar bin picking application. Adv. Sci., Technol. Eng. Syst. J. 5(6), 1244–1249 (2020)

    Article  Google Scholar 

  38. Zmarzły, P.: Technological heredity of the turning process. Tehnički Vjesnik. 27, 11–25 (2020). https://doi.org/10.17559/TV-20190425150325

    Article  Google Scholar 

Download references

Acknowledgements

We acknowledgment the support of time and facilities from Ho Chi Minh City University of Technology (HCMUT), VNU-HCM for this study

Author information

Authors and Affiliations

  1. Department of Mechatronics, Falculty of Mechanical Engineering, Ho Chi Minh City University of Technology (HCMUT), 268 Ly Thuong Kiet Street, District 10, Ho Chi Minh City, Vietnam

    Le Duc Hanh & Hoang Thien Phuc

  2. Vietnam National University, Ho Chi Minh City, Linh Trung Ward, Thu Duc District, Ho Chi Minh City, Vietnam

    Le Duc Hanh & Hoang Thien Phuc

Authors
  1. Le Duc Hanh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Hoang Thien Phuc
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

LDH: Testing, Writing and review. HTP: Coding and testing.

Corresponding author

Correspondence to Le Duc Hanh.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Ethics approval and consent to participate

The authors consent to participate in this work.

Consent for publication

The authors consent for publication.

Additional information

Publisher's Note

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

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hanh, L.D., Phuc, H.T. Simultaneously extract 3D seam curve and weld head angle for robot arm using passive vision. Int J Interact Des Manuf 16, 1125–1134 (2022). https://doi.org/10.1007/s12008-021-00801-0

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s12008-021-00801-0

Keywords

Search

Navigation