Skip to main content
Log in

A wire deflection detection method based on image processing in wire + arc additive manufacturing

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

Abstract

In wire and arc additive manufacture (WAAM), the twist of wire during a robot’s movement can result in the sudden changes of the wire-feeding position and thus cause deposition defects and dimensional errors. In the worst case, it may cause wire jamming and damage of the wire-feeding system. Therefore, online monitoring and correction of the wire deflection are very important for WAAM. In this paper, a vision-based measuring method is proposed for detecting the deviations of the wire-feeding position of a plasma welding-based WAAM process. It uses adaptive threshold and Hough transform to extract the wire edges, judges and merges the coincident lines, and applies Radon transform to measure the wire deflection. Software to automatically detect the wire deviation was developed based on the proposed method. The method and the software were verified with experiments.

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.

Similar content being viewed by others

References

  1. Frazier EW (2014) Metal additive manufacturing: a review. J Mater Eng Perform 23:1917–1928

    Article  Google Scholar 

  2. Ding D, Pan X, Cuiuri D, Li H (2015) Wire-feed additive manufacturing of metal components: technologies, developments and future interests. Int J Adv Manuf Technol 81:465–481. doi:10.1007/s00170-015-7077-3

    Article  Google Scholar 

  3. Williams SW, Martina F, Addison AC, Ding J, Pardal G, Colegrove P (2015) Wire + arc additive manufacturing. Mater Sci Technol. doi:10.1179/1743284715Y.0000000073

    Google Scholar 

  4. Ding J, Colegrove P, Mehnen J, Williams SW, Wang F, Sequeira Almeida P (2013) A computationally efficient finite element model of wire and arc additive manufacture. Int J Adv Manuf Technol 70:227–236

    Article  Google Scholar 

  5. Cong B, Ding J, Williams SW (2014) Effect of arc mode in cold metal transfer process on porosity of additively manufactured Al-6.3% Cu alloy. Int J Adv Manuf Technol 76:1593–1606

    Article  Google Scholar 

  6. Wang F, Williams SW, Rush M (2011) Morphology investigation on direct current pulsed gas tungsten arc welded additive layer manufactured Ti6Al4V alloy. Int J Adv Manuf Technol 57:597–603

    Article  Google Scholar 

  7. Kim JD, Peng Y (2000) Plunging method for Nd: YAG laser cladding with wire feeding. Opt Lasers Eng 33:299–309

    Article  Google Scholar 

  8. Syed WUH, Li L (2005) Effects of wire feeding direction and location in multiple layer diode laser direct metal deposition. Appl Surf Sci 248:518–524

    Article  Google Scholar 

  9. Hagqvist P, Heralić A, Christiansson AK, Lennartson B (2015) Resistance based iterative learning control of additive manufacturing with wire. Mechatronics 31:116–123

    Article  Google Scholar 

  10. Seyffarth P, Gaede R (2011) Image processing for automated robotic welding. In: Robotic welding, intelligence and automation. Springer Berlin Heidelberg, p 15–21

  11. Xu D, Wang L, Tan M (2004) Image processing and visual control method for arc welding robot. In Robotics and Biomimetics, 2004. ROBIO 2004. IEEE International Conference on IEEE, p 727–732

  12. Xiong J, Zhang G (2013) Online measurement of bead geometry in GMAW-based additive manufacturing using passive vision. Meas Sci Technol 24:115103. doi:10.1088/0957-0233/24/11/115103

    Article  Google Scholar 

  13. Bonaccorso F, Cantelli L, Muscato G (2011) An arc welding robot control for a shaped metal deposition plant: modular software interface and sensors. IEEE Trans Ind Electron 58(8):3126–3132

    Article  Google Scholar 

  14. Ma H, Wei S, Sheng Z, Lin T, Chen S (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):945–953

    Article  Google Scholar 

  15. Allada V, Anand S (1996) Efficient vertex detection algorithms using the Hough transform. Int J Adv Manuf Technol 11(6):394–405

    Article  Google Scholar 

  16. Ramamoorthy B (2011) An accurate and robust method for the honing angle evaluation of cylinder liner surface using machine vision. Int J Adv Manuf Technol 55(5-8):611–621

    Article  Google Scholar 

  17. Dougherty E (1999) Electronic imaging technology. SPIE Optical Engineering Press, Bellingham

    Google Scholar 

  18. Chen SB, Zhao DB, Lou YJ, Wu L (2004) Computer vision sensing and intelligent control of welding pool dynamics. In: Robotic welding, intelligence and automation. Springer Berlin, Heidelberg, pp 25–55

    Chapter  Google Scholar 

  19. Yan Z, Xu D, Li Y (2008) A weld edge feature extraction method based on adaptive binarization threshold (In Chinese). Trans China Weld Inst 29(7)

  20. Jiang B, Huang W (2007) Adaptive threshold median filter for multiple-impulse noise. J Electron Sci Technol China 5(1):70–74

    Google Scholar 

  21. Yang JD, Chen YS, Hsu WH (1994) Adaptive thresholding algorithm and its hardware implementation. Pattern Recognit Lett 15(2):141–150

    Article  MATH  Google Scholar 

  22. Maragos P (2005) Morphological filtering for image enhancement and feature detection. In: Bovic AC (ed) The image and video processing handbook. Elsevier Academic Press, Amsterdam, pp 135–156

  23. Zhao X, Chen Z, Lu J, Jing C (2012) The scale and rotating invariant auto stereo matching. Acta Geodaetica et Cartographica Sinica 41(1):81G86

    Google Scholar 

  24. Lee W C, Chen C H (2009) A fast template matching method for rotation invariance using two-stage process. In Intelligent Information Hiding and Multimedia Signal Processing, 2009. IIH-MSP’09. Fifth International Conference on IEEE, p. 9–12

  25. Gao X (2006) A sort of template matching method with rotation invariant. Machine Vision 109–112

  26. Chen Z (2006) Study on image matching technology. Central China Normal University, Wuhan

    Google Scholar 

  27. Zhao HZ, Zhang YC (2010) Image retrieval algorithm based on Canny edge detection operator. Electron Design Eng 2:034

    Google Scholar 

  28. Galambos C, Kittler J, Matas J (1999) Progressive probabilistic Hough transform for line detection. Computer Vision and Pattern Recognition, 1999. IEEE Computer Society Conference on IEEE, Vol. 1, p 1554

  29. Xu R (2009) Radon application of Radon transform in vehicle license plate lean correction. China Sci Technol Inf 12:146–147

    Google Scholar 

  30. Muhammad J, Altun H, Abo-Serie E (2016) Welding seam profiling techniques based on active vision sensing for intelligent robotic welding. Int J Adv Manuf Technol 1–19. doi:10.1007/s00170-016-8707-0

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Qiang Zhan.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhan, Q., Liang, Y., Ding, J. et al. A wire deflection detection method based on image processing in wire + arc additive manufacturing. Int J Adv Manuf Technol 89, 755–763 (2017). https://doi.org/10.1007/s00170-016-9106-2

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9106-2

Keywords

Navigation