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A new passive vision weld seam tracking method for FSW based on K-means

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Abstract

In this paper, a dual-camera-guided weld start point identification and weld tracking method based on K-means passive vision is proposed to improve the problem that structured light visual guidance is not applicable in stir friction welding due to the severe reflection on the surface of narrow weld aluminum alloy. This method is mainly applied to narrow weld seam stir friction welding where no bright light or smoke is generated, because it has many advantages over methods that use structured light to guide the welding process: the ability to extract more information about the weld seam and the ability to identify narrower weld seams. By pre-processing operations such as noise reduction and filtering of the target image, K-means is used to obtain information on the start point of the weld seam. The weld seam is tracked in real time after the coordinates are converted and the device is controlled to reach the target point. The experimental results show that the method can effectively identify the start point and path of the weld, and has high accuracy for tracking complex narrow weld seams, and has great potential to improve production efficiency.

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References

  1. Mohan DG, Wu C (2021) A review on friction stir welding of steels. Chin J Mech Eng 34:137. https://doi.org/10.1186/s10033-021-00655-3

    Article  Google Scholar 

  2. Mishra D, Roy RB, Dutta S, Pal SK, Chakravarty D (2018) A review on sensor based monitoring and control of friction stir welding process and a roadmap to Industry 4.0. J Manuf Process 36:373–397. https://doi.org/10.1016/j.jmapro.2018.10.016

    Article  Google Scholar 

  3. Mendes N, Neto P, Simão MA, Loureiro A, Pires JN (2016) A novel friction stir welding robotic platform: welding polymeric materials. Int J Adv Manuf Technol 85:37–46. https://doi.org/10.1007/s00170-014-6024-z

    Article  Google Scholar 

  4. Bagheri B, Abdollahzadeh A, Abbasi M, Kokabi AH (2020) Numerical analysis of vibration effect on friction stir welding by smoothed particle hydrodynamics (SPH). Int J Adv Manuf Technol 110:209–228. https://doi.org/10.1007/s00170-020-05839-0

    Article  Google Scholar 

  5. Dadaei M, Omidvar H, Bagheri B, Jahazi M, Abbasi M (2014) The effect of SiC/Al2O3 particles used during FSP on mechanical properties of AZ91 magnesium alloy. Int J Mater Res 105:369–374. https://doi.org/10.3139/146.111025

    Article  Google Scholar 

  6. Bagheri B, Abdollahzadeh A, Sharifi F, Abbasi M, Moghaddam AO (2021) Recent development in friction stir processing of aluminum alloys: microstructure evolution, mechanical properties, wear and corrosion behaviors. Proc Inst Mech Eng Part E-J Process Mech Eng. https://doi.org/10.1177/09544089211058007

  7. Abbasi M, Givi M, Bagheri B (2020) New method to enhance the mechanical characteristics of Al-5052 alloy weldment produced by tungsten inert gas. Proc Inst Mech Eng Part B-J Eng Manuf. https://doi.org/10.1177/0954405420929777

  8. Bagheri B, Abbasi M, Dadaei M (2020) Mechanical behavior and microstructure of AA6061-T6 joints made by friction stir vibration welding. J Mater Eng Perform 29:1165–1175. https://doi.org/10.1007/s11665-020-04639-7

    Article  Google Scholar 

  9. Chen XZ, Chen SB (2010) The autonomous detection and guiding of start welding position for arc welding robot. Ind Robot 37:70–78. https://doi.org/10.1108/01439911011009975

    Article  Google Scholar 

  10. Wei S, Ma H, Lin T, Chen S (2010) Autonomous guidance of initial welding position with “single camera and double positions” method. Sens Rev 30:62–68. https://doi.org/10.1108/02602281011010808

    Article  Google Scholar 

  11. Wu C, Yang P, Lei T, Zhu D, Zhou Q, Zhao S (2022) A teaching-free welding position guidance method for fillet weld based on laser vision sensing and EGM technology. Optik 262:169291. https://doi.org/10.1016/j.ijleo.2022.169291

    Article  Google Scholar 

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

    Article  Google Scholar 

  13. Fan J, Jing F, Yang L, Long T, Tan M (2019) A precise seam tracking method for narrow butt seams based on structured light vision sensor. Opt Laser Technol 109:616–626. https://doi.org/10.1016/j.optlastec.2018.08.047

    Article  Google Scholar 

  14. He Y, Yu Z, Li J, Yu L, Ma G (2020) Discerning weld seam profiles from strong arc background for the robotic automated welding process via visual attention features. Chin J Mech Eng 33:21. https://doi.org/10.1186/s10033-020-00438-2

    Article  Google Scholar 

  15. 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 

  16. Tian Y, Liu H, Li L, Yuan G, Feng J, Chen Y, Wang W (2021) Automatic identification of multi-type weld seam based on vision sensor with silhouette-mapping. IEEE Sensors J 21:5402–5412. https://doi.org/10.1109/JSEN.2020.3034382

    Article  Google Scholar 

  17. Li X, Li X, Ge SS, Khyam MO, Luo C (2017) Automatic welding seam tracking and identification. IEEE Trans Ind Electron 64:7261–7271. https://doi.org/10.1109/TIE.2017.2694399

    Article  Google Scholar 

  18. Wang N, Shi X, Zhong K, Zhang X, Chen W (2021) A path correction method based on global and local matching for robotic autonomous systems. J Intell Robot Syst 104:7. https://doi.org/10.1007/s10846-021-01537-5

    Article  Google Scholar 

  19. He Y, Ma G, Chen S (2022) Autonomous decision-making of welding position during multipass GMAW with t-joints: a Bayesian network approach. IEEE Trans Ind Electron 69:3909–3917. https://doi.org/10.1109/TIE.2021.3076710

    Article  Google Scholar 

  20. Xiao R, Xu Y, Hou Z, Xu F, Zhang H, Chen S (2022) A novel visual guidance framework for robotic welding based on binocular cooperation. Robot Comput-Integr Manuf 78:102393. https://doi.org/10.1016/j.rcim.2022.102393

    Article  Google Scholar 

  21. Tian Y-Z, Liu H-F, Li L, Wang W-B, Feng J-C, Xi F-F, Yuan G-J (2020) Robust identification of weld seam based on region of interest operation. Adv Manuf 8:473–485. https://doi.org/10.1007/s40436-020-00325-y

    Article  Google Scholar 

  22. Shen H, Wu J, Lin T, Chen S (2008) Arc welding robot system with seam tracking and weld pool control based on passive vision. Int J Adv Manuf Technol 39:669–678. https://doi.org/10.1007/s00170-007-1257-8

    Article  Google Scholar 

  23. Sinaga KP, Yang M-S (2020) Unsupervised K-means clustering algorithm. IEEE. Access 8:80716–80727. https://doi.org/10.1109/ACCESS.2020.2988796

    Article  Google Scholar 

  24. Ma Y, Fan J, Yang H, Yang L, Ji Z, Jing F, Tan M (2021) A fast and robust seam tracking method for spatial circular weld based on laser visual sensor. IEEE Trans Instrum Meas 70:1–11. https://doi.org/10.1109/TIM.2021.3106685

    Article  Google Scholar 

  25. Zhang W, Sun C (2021) Corner detection using second-order generalized Gaussian directional derivative representations. IEEE Trans Pattern Anal Mach Intell 43:1213–1224. https://doi.org/10.1109/TPAMI.2019.2949302

    Article  Google Scholar 

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Funding

This research is supported by the Fundamental Research Funds for the Central Universities of China (Grant No. 2022CDJQY-014), and 2022 Jiangsu Provincial Science and technology plan special fund (grant number BE2022110).

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Authors and Affiliations

  1. College of Material Science and Engineering, Chongqing University, Chongqing, 400044, China

    Liu Yang, Jinchun Deng & Jun Shen

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  1. Liu Yang
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  2. Jinchun Deng
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  3. Jun Shen
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Contributions

All authors contributed to the study conception and design. Material preparation, data collection, and analysis were performed by [Jun Shen], [Liu Yang], and [Chunjin Deng]. The first draft of the manuscript was written by [Liu Yang] and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

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Correspondence to Jun Shen.

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Yang, L., Deng, J. & Shen, J. A new passive vision weld seam tracking method for FSW based on K-means. Int J Adv Manuf Technol 128, 3283–3295 (2023). https://doi.org/10.1007/s00170-023-12169-4

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  • DOI: https://doi.org/10.1007/s00170-023-12169-4

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