Skip to main content
Log in

Research on the deviation sensing of V-groove weld seam based on a novel two channel acoustic sensor

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

It is essential to sense the deviation of weld seam real timely in robotic welding process. However, welding process always accompanied with high temperature, strong arc light, and background noises, which significantly affects the application of sensors. In this study, a novel acoustic sensor was developed. This sensor consists of two microphones. Based on the sound signals collected by these two microphones, the deviation of weld seam was detected. The frequency response of the developed acoustic sensor was studied through simulation method firstly, and then, the sensing performance of it was analyzed with experiments. The experimental results show that the developed acoustic sensor has a linear property for the deviation detection of V-groove weld seam. This research provides a novel method for weld seam tracking.

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

source frequency is 2001 Hz. (b) Sound source frequency is 6201 Hz. (c) Sound source frequency is 11601 Hz. (d) Sound source frequency is 17201 Hz

Fig. 5
Fig. 6

source in the center of weld seam. (b) Sound source at the right side of weld seam

Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18
Fig. 19
Fig. 20
Fig. 21
Fig. 22
Fig. 23
Fig. 24
Fig. 25

Similar content being viewed by others

References

  1. Presern S, Gyergyek L (1983) An intelligent tactile sensor-an on-line hierarchical object and seam analyzer. IEEE Trans Pattern Anal Mach Intell 2:217–220

    Article  Google Scholar 

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

  3. Kim CH, Na SJ (2001) A study of an arc sensor model for gas metal arc welding with rotating arc Part 1: dynamic simulation of wire melting. Proc Inst Mech Eng Part B J Eng Manuf 215:1271–1279. https://doi.org/10.1243/0954405011519321

    Article  Google Scholar 

  4. Shi YH, Yoo WS, Na SJ (2006) Mathematical modelling of rotational arc sensor in GMAW and its applications to seam tracking and endpoint detection. Sci Technol Weld Join 11:723–730. https://doi.org/10.1179/174329306X153196

    Article  Google Scholar 

  5. Kodama S, Ichiyama Y, Ikuno Y, Baba N (2006) Arc sensor sensitivity in short circuiting metal active gas welding with high speed torch oscillation. Sci Technol Weld Join 11:25–32. https://doi.org/10.1179/174329306X77867

    Article  Google Scholar 

  6. Gao Y, Zhang H, Mao Z (2009) Fillet welding seam tracking based on a mobile robot with rotational arc sensor. Jixie Gongcheng Xuebao/Journal Mech Eng 45:64–71. https://doi.org/10.3901/JME.2009.09.064

    Article  Google Scholar 

  7. Chen SB, Zhang Y, Qiu T, Lin T (2003) Robotic welding systems with vision-sensing and self-learning neuron control of arc welding dynamic process. J Intell Robot Syst Theory Appl 36:191–208. https://doi.org/10.1023/A:1022652706683

    Article  Google Scholar 

  8. Wu QQ, Lee JP, Park MH et al (2015) A study on the modified Hough algorithm for image processing in weld seam tracking. J Mech Sci Technol 29:4859–4865. https://doi.org/10.1007/s12206-015-1033-x

    Article  Google Scholar 

  9. Jia Z, Wang T, He J et al (2020) Real-time spatial intersecting seam tracking based on laser vision stereo sensor. Meas J Int Meas Confed 149:106987. https://doi.org/10.1016/j.measurement.2019.106987

    Article  Google Scholar 

  10. 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 21:302–315. https://doi.org/10.1016/s0278-6125(02)80169-8

    Article  Google Scholar 

  11. Chen H, Lin T, Chen S (2011) Seam tracking and dynamic process control for high precision arc welding. In: Robotic Welding, Intelligence and Automation. pp 193–201

  12. Xu Y, Fang G, Chen S et al (2014) Real-time image processing for vision-based weld seam tracking in robotic GMAW. Int J Adv Manuf Technol 73:1413–1425. https://doi.org/10.1007/s00170-014-5925-1

    Article  Google Scholar 

  13. Song S, Chen H, Lin T et al (2016) Penetration state recognition based on the double-sound-sources characteristic of VPPAW and hidden Markov Model. J Mater Process Technol 234:33–44. https://doi.org/10.1016/j.jmatprotec.2016.03.002

    Article  Google Scholar 

  14. Lv N, Xu Y, Li S et al (2017) Automated control of welding penetration based on audio sensing technology. J Mater Process Technol 250:81–98. https://doi.org/10.1016/j.jmatprotec.2017.07.005

    Article  Google Scholar 

  15. Zhang Z, Wen G, Chen S (2018) Audible sound-based intelligent evaluation for aluminum alloy in robotic pulsed GTAW: mechanism, feature selection, and defect detection. IEEE Trans Ind Informatics 14:2973–2983. https://doi.org/10.1109/TII.2017.2775218

    Article  Google Scholar 

  16. Gao Y, Zhao J, Wang Q et al (2020) Weld bead penetration identification based on human-welder subjective assessment on welding arc sound. Meas J Int Meas Confed 154:107475. https://doi.org/10.1016/j.measurement.2020.107475

    Article  Google Scholar 

  17. Lan H, Zhang H, Chen S et al (2014) Correlation of arc sound and arc-sidewall position in narrow gap MAG welding. Chinese J Mech Eng 50:38–43

    Article  Google Scholar 

  18. Liu W, Guan Z, Jiang X et al (2019) Research on the seam tracking of narrow gap P-GMAW based on arc sound sensing. Sensors Actuators A Phys 292:205–216. https://doi.org/10.1016/j.sna.2019.04.015

    Article  Google Scholar 

  19. Na L, Gu F, Yan-ling X et al (2017) Real-time monitoring of welding path in pulse metal-inert gas robotic welding using a dual-microphone array. Int J Adv Manuf Technol 90:2955–2968. https://doi.org/10.1007/s00170-016-9571-7

    Article  Google Scholar 

Download references

Funding

This work was supported by National Natural Science Foundation of Shanghai (21ZR1425900).

Author information

Authors and Affiliations

Authors

Contributions

Yanfeng Gao: Conceptualization, Methodology, Writing-original draft preparation, Funding acquisition. Jianhua Xiao: Investigation, Data curation, Visualization. Genliang Xiong: Writing-review and Editing. Hua Zhang: Supervision, Project administration.

Corresponding authors

Correspondence to Yanfeng Gao or Jianhua Xiao.

Ethics declarations

Ethics approval

Not applicable.

Consent to participate

All the authors listed have participated in the preparation of the manuscript.

Consent for publication

This manuscript is approved by all the authors for publication. It is the original research that has not been published previously, and not under consideration for publication elsewhere, in whole or in part.

Conflict of interest/Competing interests.

The authors declare no competing interests.

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

Gao, Y., Xiao, J., Xiong, G. et al. Research on the deviation sensing of V-groove weld seam based on a novel two channel acoustic sensor. Int J Adv Manuf Technol 119, 5821–5837 (2022). https://doi.org/10.1007/s00170-021-08454-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-021-08454-9

Keywords

Navigation