Advertisement

Vision-Based Seam Tracking in Robotic Welding: A Review of Recent Research

  • Ziheng Wang
  • Yanling XuEmail author
Conference paper
Part of the Transactions on Intelligent Welding Manufacturing book series (TRINWM)

Abstract

Robotic welding is widely used in industrial automation, and weld seam tracking is key to the robotic welding and needs to be solved urgently. Because of high precision and adaptability, vision-based seam tracking has become the most widely used technology in weld seam tracking. Researches on vision-based seam tracking have been conducted by many scholars and progressive results have been obtained. Aimed at key problems of seam tracking, this paper summarizes the relevant research work in recent years, especially the application of active vision and passive vision. This paper focuses on the advantages and defects of these two typical methods and discusses recent outstanding progress on seam tracking techniques. In addition, the possibility of the composite vision method of active vision and passive vision in tracking is also discussed, and development directions of intelligent weld seam tracking technology are prospected.

Keyword

Robotic welding Seam tracking Passive vision Active vision Composite visual sensing 

Notes

Acknowledgements

This work is partly supported by the National Natural Science Foundation of China (61973213), and the Shanghai Natural Science Foundation (18ZR1421500).

References

  1. 1.
    Chen SB, Lv N (2014) Research evolution on intelligentized technologies for arc welding process. J Manuf Process 16(1):109–122CrossRefGoogle Scholar
  2. 2.
    Kotera S (2018) Teaching system and teaching method of welding robot. US Patent Application 15/951,862, 25 Oct 2018Google Scholar
  3. 3.
    Ban K (2018) Programming device and robot control method. US Patent Application 15/948,046, 22 Nov 2018Google Scholar
  4. 4.
    Zhang W, Dong Z, Liu Z (2017) Present situation and development trend of welding robot. In: 2017 2nd international conference on materials science, machinery and energy engineering (MSMEE 2017). Atlantis PressGoogle Scholar
  5. 5.
    Lai R, Lin W, Wu Y (2018) Review of research on the key technologies, application fields and development trends of intelligent robots. In: International conference on intelligent robotics and applications, vol 1. Springer, Cham, pp 449–458CrossRefGoogle Scholar
  6. 6.
    Almassri AM, Wan Hasan WZ, Ahmad SA et al (2015) Pressure sensor: state of the art, design, and application for robotic hand. J Sens 1:1–10CrossRefGoogle Scholar
  7. 7.
    Shelyagin V, Zaitsev I, Bernatskyi A, et al (2018) Contactless monitoring of welding processes with computer processing of acoustic emission signals. In: 2018 14th international conference on advanced trends in radio electronics, telecommunications and computer engineering (TCSET), vol 1. IEEE, pp 706–710Google Scholar
  8. 8.
    Shi Y, Zhang G, Li C, et al (2015) Weld pool oscillation frequency in pulsed gas tungsten arc welding with varying weld penetration. In: 2015 IEEE international conference on automation science and engineering (CASE), vol 1. IEEE, pp 401–406Google Scholar
  9. 9.
    Le J, Zhang H, Chen X (2017) Right-angle fillet weld tracking by robots based on rotating arc sensors in GMAW. Int J Adv Manuf Technol 93(1–4):605–616CrossRefGoogle Scholar
  10. 10.
    Soares LB, Weis ÁA, Rodrigues RN et al (2017) Seam tracking and welding bead geometry analysis for autonomous welding robot. In: 2017 Latin American robotics symposium (LARS) and 2017 Brazilian symposium on robotics (SBR), vol 1. IEEE, pp 1–6Google Scholar
  11. 11.
    Shah HNM, Sulaiman M, Shukor AZ et al (2016) Review paper on vision based identification, detection and tracking of weld seams path in welding robot environment. Mod Appl Sci 10(2):83–89CrossRefGoogle Scholar
  12. 12.
    Chen SB (2011) Research evolution on intelligentized technologies for robotic welding at SJTU. In: Robotic welding, intelligence and automation. Springer, Berlin, pp 3–14Google Scholar
  13. 13.
    Zhong J, Xu Y, Chen H et al (2019) Based on multi-sensor of roughness set model of aluminium alloy pulsed GTAW seam forming control research. In: Transactions on intelligent welding manufacturing, vol 1. Springer, Singapore, pp 39–57CrossRefGoogle Scholar
  14. 14.
    Pérez L, Rodríguez Í, Rodríguez N et al (2016) Robot guidance using machine vision techniques in industrial environments: a comparative review. Sensors 16(3):335CrossRefGoogle Scholar
  15. 15.
    Gong Y, Lin Z, Wang J et al (2018) Bringing machine intelligence to welding visual inspection: development of low-cost portable embedded device for welding quality control. Electron Imaging 9:1–4CrossRefGoogle Scholar
  16. 16.
    Tarn TJ, Chen SB (2007) Robotic welding, intelligence and automation. Springer, BerlinzbMATHCrossRefGoogle Scholar
  17. 17.
    Fridenfalk M (2003) Development of intelligent robot systems based on sensor control. Univ Google Scholar
  18. 18.
    Chaki S, Shanmugarajan B, Ghosal S et al (2015) Application of integrated soft computing techniques for optimization of hybrid CO2 laser–MIG welding process. Appl Soft Comput 30:365–374CrossRefGoogle Scholar
  19. 19.
    Dinham M, Fang G (2013) Autonomous weld seam identification and localization using eye-in-hand stereo vision for robotic arc welding. Robot Comput-Integr Manuf 29(5):288–301CrossRefGoogle Scholar
  20. 20.
    Shen H, Lin T, Chen S et al (2010) Real-time seam tracking technology of welding robot with visual sensing. J Intell Rob Syst 59(3–4):283–298zbMATHCrossRefGoogle Scholar
  21. 21.
    Du R, Xu Y, Hou Z et al (2019) Strong noise image processing for vision-based seam tracking in robotic gas metal arc welding. Int J Adv Manuf Technol 101(5–8):2135–2149CrossRefGoogle Scholar
  22. 22.
    Xu Y, Lv N, Han Y et al (2016) Research on the key technology of vision sensor in robotic welding. In: 2016 IEEE workshop on advanced robotics and its social impacts (ARSO). IEEE, pp 121–125Google Scholar
  23. 23.
    Rout A, Deepak B, Biswal BB (2019) Advances in weld seam tracking techniques for robotic welding: a review. Robot Comput-Integr Manuf 56:12–37CrossRefGoogle Scholar
  24. 24.
    Muhammad J, Altun H, Abo-Serie E (2017) Welding seam profiling techniques based on active vision sensing for intelligent robotic welding. Int J Adv Manuf Technol 88(1–4):127–145CrossRefGoogle Scholar
  25. 25.
    Shen H, Wu J, Lin T et al (2008) Arc welding robot system with seam tracking and weld pool control based on passive vision. Int J Adv Manuf Technol 39(7–8):669–678CrossRefGoogle Scholar
  26. 26.
    Chen SB, Zhang Y, Qiu T et al (2003) Robotic welding systems with vision-sensing and self-learning neuron control of arc welding dynamic process. J Intell Rob Syst 36(2):191–208CrossRefGoogle Scholar
  27. 27.
    Ma H, Wei S, Sheng Z et al (2010) Robot welding seam tracking method based on passive vision for thin plate closed-gap butt welding. Int J Adv Manuf Technol 48(9–12):945–953CrossRefGoogle Scholar
  28. 28.
    Ye Z, Fang G, Chen S et al (2013) Passive vision-based seam tracking system for pulse-MAG welding. Int J Adv Manuf Technol 67(9–12):1987–1996CrossRefGoogle Scholar
  29. 29.
    Jin Z, Li H, Zhang C et al (2017) Online welding path detection in automatic tube-to-tubesheet welding using passive vision. Int J Adv Manuf Technol 90(9–12):3075–3084CrossRefGoogle Scholar
  30. 30.
    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(9–12):1413–1425CrossRefGoogle Scholar
  31. 31.
    Xu Y, Yu H, Zhong J et al (2012) Real-time seam tracking control technology during welding robot GTAW process based on passive vision sensor. J Mater Process Technol 212(8):1654–1662CrossRefGoogle Scholar
  32. 32.
    Nele L, Sarno E, Keshari A (2013) An image acquisition system for real-time seam tracking. Int J Adv Manuf Technol 69(9–12):2099–2110CrossRefGoogle Scholar
  33. 33.
    Chen H, Liu K, Xing G et al (2014) A robust visual servo control system for narrow seam double head welding robot. Int J Adv Manuf Technol 71(9–12):1849–1860CrossRefGoogle Scholar
  34. 34.
    Liu J, Fan Z, Olsen S I, et al (2015) A real-time passive vision system for robotic arc welding. In: 2015 IEEE international conference on automation science and engineering (CASE). IEEE, pp 389–394Google Scholar
  35. 35.
    Lin L, Bingqiang L, Yanbiao Z (2015) Study on seam tracking system based on stripe type laser sensor and welding robot. Chin J Lasers 42(5):1–8Google Scholar
  36. 36.
    Zou Y, Wang Y, Zhou W et al (2018) Real-time seam tracking control system based on line laser visions. Opt Laser Technol 103:182–192CrossRefGoogle Scholar
  37. 37.
    Zou Y, Chen X, Gong G et al (2018) A seam tracking system based on a laser vision sensor. Measurement 127:489–500CrossRefGoogle Scholar
  38. 38.
    Zhang L, Ke W, Han Z et al (2013) A cross structured light sensor for weld line detection on wall-climbing robot. In: 2013 IEEE international conference on mechatronics and automation. IEEE, pp 1179–1184Google Scholar
  39. 39.
    Kiddee P, Fang Z, Tan M (2016) An automated weld seam tracking system for thick plate using cross mark structured light. Int J Adv Manuf Technol 87(9–12):3589–3603CrossRefGoogle Scholar
  40. 40.
    Xu P, Xu G, Tang X et al (2008) A visual seam tracking system for robotic arc welding. Int J Adv Manuf Technol 37(1–2):70–75CrossRefGoogle Scholar
  41. 41.
    Xu P, Tang X, Yao S (2008) Application of circular laser vision sensor (CLVS) on welded seam tracking. J Mater Process Technol 205(1–3):404–410CrossRefGoogle Scholar
  42. 42.
    Zhang C, Li H, Jin Z et al (2017) Seam sensing of multi-layer and multi-pass welding based on grid structured laser. Int J Adv Manuf Technol 91(1–4):1103–1110CrossRefGoogle Scholar
  43. 43.
    Soares LB, Weis ÁA, Rodrigues RN et al (2017) Seam tracking and welding bead geometry analysis for autonomous welding robot. In: 2017 Latin American robotics symposium (LARS) and 2017 Brazilian symposium on robotics (SBR). IEEE, pp 1–6Google Scholar
  44. 44.
    Lü X, Gu D, Wang Y et al (2018) Feature extraction of welding seam image based on laser vision. IEEE Sens J 18(11):4715–4724CrossRefGoogle Scholar
  45. 45.
    Li X, Li X, Ge SS et al (2017) Automatic welding seam tracking and identification. IEEE Trans Ind Electron 64(9):7261–7271CrossRefGoogle Scholar
  46. 46.
    Aviles-Viñas JF, Rios-Cabrera R, Lopez-Juarez I (2016) On-line learning of welding bead geometry in industrial robots. Int J Adv Manuf Technol 83(1–4):217–231CrossRefGoogle Scholar
  47. 47.
    Aviles-Viñas JF, Lopez-Juarez I, Rios-Cabrera R (2015) Acquisition of welding skills in industrial robots. Ind Robot: Int J 42(2):156–166CrossRefGoogle Scholar
  48. 48.
    Zhang L, Xu Y, Du S et al (2018) Point cloud based three-dimensional reconstruction and identification of initial welding position. In: Transactions on intelligent welding manufacturing. Springer, Singapore, pp 61–77CrossRefGoogle Scholar
  49. 49.
    Fan J, Jing F, Fang Z et al (2017) Automatic recognition system of welding seam type based on SVM method. Int J Adv Manuf Technol 92(1–4):989–999CrossRefGoogle Scholar
  50. 50.
    Shah HNM, Sulaiman M, Shukor AZ (2017) Autonomous detection and identification of weld seam path shape position. Int J Adv Manuf Technol 92(9–12):3739–3747CrossRefGoogle Scholar
  51. 51.
    Shah HNM, Sulaiman M, Shukor AZ et al (2018) Butt welding joints recognition and location identification by using local thresholding. Robot Comput-Integr Manuf 51:181–188CrossRefGoogle Scholar
  52. 52.
    Zeng J, Chang B, Du D et al (2017) A vision-aided 3D path teaching method before narrow butt joint welding. Sensors 17(5):1099CrossRefGoogle Scholar
  53. 53.
    Dittrich D, Schedewy R, Brenner B et al (2013) Laser-multi-pass-narrow-gap-welding of hot crack sensitive thick aluminum plates. Phys Procedia 41:225–233CrossRefGoogle Scholar
  54. 54.
    Gu WP, Xiong ZY, Wan W (2013) Autonomous seam acquisition and tracking system for multi-pass welding based on vision sensor. Int J Adv Manuf Technol 69(1–4):451–460CrossRefGoogle Scholar
  55. 55.
    He Y, Xu Y, Chen Y et al (2016) Weld seam profile detection and feature point extraction for multi-pass route planning based on visual attention model. Robot Comput-Integr Manuf 37:251–261CrossRefGoogle Scholar
  56. 56.
    He Y, Chen Y, Xu Y et al (2016) Autonomous detection of weld seam profiles via a model of saliency-based visual attention for robotic arc welding. J Intell Rob Syst 81(3–4):395–406CrossRefGoogle Scholar
  57. 57.
    Zeng J, Chang B, Du D et al (2018) A weld position recognition method based on directional and structured light information fusion in multi-layer/multi-pass welding. Sensors 18(1):129CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Engineering and Applied ScienceUniversity of PennsylvaniaPhiladelphiaUSA
  2. 2.Intelligentized Robotic Welding Technology Laboratory, School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina
  3. 3.Shanghai Key Laboratory of Materials Laser Processing and Modification, School of Materials Science and EngineeringShanghai Jiao Tong UniversityShanghaiChina

Personalised recommendations