Skip to main content

Advertisement

Springer Nature Link
Log in

A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing

  • Published:
Advances in Manufacturing Aims and scope Submit manuscript

Abstract

The real-time monitoring of the weld pool during deposition is important for automatic control in plasma arc additive manufacturing. To obtain a high deposition accuracy, it is essential to maintain a stable weld pool size. In this study, a novel passive visual method is proposed to measure the weld pool length. Using the proposed method, the image quality was improved by designing a special visual system that employed an endoscope and a camera. It also includes pixel brightness-based and gradient-based algorithms that can adaptively detect feature points at the boundary when the weld pool geometry changes. This algorithm can also be applied to materials with different solidification characteristics. Calibration was performed to measure the real weld pool length in world coordinates, and outlier rejection was performed to increase the accuracy of the algorithm. Additionally, tests were carried out on the intersection component, and the results showed that the proposed method performed well in tracking the changing weld pool length and was applicable to the real-time monitoring of different types of materials.

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

Access this article

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

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
Fig. 16

Similar content being viewed by others

Abbreviations

CDL:

Center detecting line

CNC:

Computer numerical control

ROI:

Region of interest

DL:

Detecting line

\(b\) :

Interval between two pixels when determining the gradient

\({b}_{\mathrm{c}}\) :

Bias of the CDL in the image coordinate

\({b}_{\mathrm{r}}\) :

Bias of the reference line in the world coordinate

\(B_{\text{a}}^n(k)\) :

Smoothed brightness

\({B}_{\mathrm{max}}\) :

Maximum global brightness of all iterations

\({B}^{n}(k)\) :

Discrete brightness function along the \({n}\text{th}\) DL

\({c}_{x}\),\({c}_{y}\) :

Displacement away from the axis

\(d\) :

Distance between each DL

\(\mathrm{d}x\),\(\mathrm{d}y\) :

DL axis increment based on the CDL

\({f}_{\mathrm{s}}(x)\) :

Smooth function

\({f}_{x}\),\({f}_{y}\) :

Focal length on the optic axis

\({G}_{\mathrm{min}}\) :

Minimum global gradient of all iterations

\({G}^{n}\left(k\right)\) :

Pixel gradient

\({G}_{\rm{a}}^{n}(k)\) :

Smoothed gradient

\(H\) :

3×3 Homography matrix

\(W\) :

Array sliding over \({B}_{\rm{a}}^{n}(k)\) and \({G}_{\rm{a}}^{n}(k)\)

\(j\) :

Index in W

\({k}_{\mathrm{c}}\) :

Bias of the CDL in the image coordinate

\({k}_{\mathrm{r}}\) :

Slope of the reference line in the world coordinate

\({L}_{\mathrm{d}}\) :

Distance used to define the points falling on DL

\({M}_{n}\) :

Number of pixels points in \({P}_{\mathrm{DL}}^{n}\)

\(n\) :

Index of the DLs

\(N\) :

Number of DLs on one side of the CDL

\(O,X,Y\) :

Origin and axis of the world coordinate

\(o,x,y\) :

Origin and axis of the image coordinate

\(P(x,y)\) :

Point within the ROI

\({P}_{\mathrm{c}1}\left({x}_{\mathrm{c}1},{y}_{\mathrm{c}1}\right)\) :

Starting point of the CDL

\({P}_{\mathrm{c}2}\left({x}_{\mathrm{c}2},{y}_{\mathrm{c}2}\right)\) :

Ending point of the CDL

\({P}_{\mathrm{DL}}^{n}\) :

Points falling on the nth DL

\({P}_{\mathrm{L}1}^{n}\),\({P}_{\mathrm{L}2}^{n}\) :

End points of the nth DL

\({P}_{\mathrm{L}},{P}_{\mathrm{L}1},{P}_{\mathrm{L}2}\) :

Molten length and its two portions

\({P}_{\mathrm{R}1}\left({x}_{\mathrm{R}1},{y}_{\mathrm{R}1}\right)\) :

Upper left corner point of the ROI

\({P}_{\mathrm{R}2}\left({x}_{\mathrm{R}2},{y}_{\mathrm{R}1}\right)\) :

Upper right corner point of the ROI

\({P}_{\mathrm{R}3}\left({x}_{\mathrm{R}2},{y}_{\mathrm{R}2}\right)\) :

Lower right corner point of the ROI

\({P}_{\mathrm{R}4}\left({x}_{\mathrm{R}1},{y}_{\mathrm{R}2}\right)\) :

Lower left corner point of the ROI

\({{\varvec{r}}}_{1}\), \({{\varvec{r}}}_{2}, \, {{\varvec{r}}}_{3}\) :

3×1 vectors of the rotation transformation

\(s\) :

Scale factor

\({\varvec{t}}\) :

3×1 vector of the translation transformation

\({w}_{\mathrm{d}}\) :

Width of \(W\)

\({X}_{\mathrm{m}}\),\({Y}_{\mathrm{m}}\) :

Mean coordinates of the remaining feature points

x n, \({y}_{{n}}\) :

Image coordinates of the detected feature points

X n, Y n :

World coordinates of the detected feature points

\({\mu }_{y}\) :

Mean of \({Y}_{n}\)

\({\sigma }_{y}\) :

Standard deviation of \({Y}_{n}\)

References

  1. Wang C, Suder W, Ding J et al (2021) Wire based plasma arc and laser hybrid additive manufacture of Ti-6Al-4V. J Mater Process Tech 293:117080. https://doi.org/10.1016/j.jmatprotec.2021.117080

    Article  Google Scholar 

  2. Artaza T, Alberdi A, Murua M et al (2017) Design and integration of WAAM technology and in situ monitoring system in a gantry machine. Procedia Manuf 13:778–785

    Article  Google Scholar 

  3. Devesse W, Baere DD, Hinderdael M et al (2016) Hardware-in-the-loop control of additive manufacturing processes using temperature feedback. J Laser Appl 28(2):022302. https://doi.org/10.2351/1.4943911

    Article  Google Scholar 

  4. Demir AG, Mazzoleni L, Caprio L et al (2019) Complementary use of pulsed and continuous wave emission modes to stabilize melt pool geometry in laser powder bed fusion. Opt Laser Technol 113:15–26

    Article  Google Scholar 

  5. Hu D, Kovacevic R (2003) Modelling and measuring the thermal behaviour of the molten pool in closed-loop controlled laser-based additive manufacturing. Proc Inst Mech Eng B-J Eng 217(4):441–452

    Article  Google Scholar 

  6. Venturini G, Montevecchi F, Scippa A et al (2016) Optimization of WAAM deposition patterns for T-crossing features. Procedia Cirp 55:95–100

    Article  Google Scholar 

  7. McAndrew AR, Rosales MA, Colegrove PA et al (2018) Interpass rolling of Ti-6Al-4V wire+arc additively manufactured features for microstructural refinement. Addit Manuf 21:340–349

    Google Scholar 

  8. Li R, Wang G, Ding Y et al (2020) Optimization of the geometry for the end lateral extension path strategy to fabricate intersections using laser and cold metal transfer hybrid additive manufacturing. Addit Manuf 36:101546. https://doi.org/10.1016/j.addma.2020.101546

    Article  Google Scholar 

  9. Davis T, Shin YC (2010) Vision-based clad height measurement. Mach Vision Appl 22(1):129–136

    Article  Google Scholar 

  10. Mazzoleni L, Caprio L, Pacher M et al (2019) External illumination strategies for melt pool geometry monitoring in SLM. JOM 71:928–937

    Article  Google Scholar 

  11. Wang ZZ (2014) Monitoring of GMAW weld pool from the reflected laser lines for real-time control. IEEE T Ind Inform 10(4):2073–2083

    Article  Google Scholar 

  12. Xiong J, Shi M, Liu Y et al (2020) Virtual binocular vision sensing and control of weld pool width for gas metal arc additive manufactured thin-walled components. Addit Manuf 33:101121. https://doi.org/10.1016/j.addma.2020.101121

    Article  Google Scholar 

  13. Yang D, Wang G, Zhang G (2017) Thermal analysis for single-pass multi-layer GMAW based additive manufacturing using infrared thermography. J Mater Process Tech 244:215–224

    Article  Google Scholar 

  14. Chen ZQ, Gao XD (2014) Detection of weld pool width using infrared imaging during high-power fiber laser welding of type 304 austenitic stainless steel. Int J Adv Manuf Tech 74(9/12):1247–1254

    Article  Google Scholar 

  15. Yamane S, Godo T, Hosoya T et al (2015) Detecting and tracking of welding line in visual plasma robotic welding. Weld Int 29(9):661–667

    Article  Google Scholar 

  16. Zhang GK, Wu CS, Liu X (2020) Single vision system for simultaneous observation of keyhole and weld pool in plasma arc welding. J Mater Process Tech 215:71–78

    Article  Google Scholar 

  17. Xiong J, Pi Y, Chen H (2019) Deposition height detection and feature point extraction in robotic GTA- based additive manufacturing using passive vision sensing. Robot Com-Int Manuf 59:326–334

    Article  Google Scholar 

  18. Reisgen U, Purrio M, Buchholz G et al (2020) Machine vision system for online weld pool observation of gas metal arc welding processes. Weld World 58(5):707–711

    Article  Google Scholar 

  19. Akbari M, Saedodin S, Toghraie D et al (2014) Experimental and numerical investigation of temperature distribution and melt pool geometry during pulsed laser welding of Ti6Al4V alloy. Opt Laser Technol 59:52–59

    Article  Google Scholar 

  20. Liu BQ, Fang G, Lei LP (2021) An analytical model for rapid predicting molten pool geometry of selective laser melting (SLM). Appl Math Model 92:505–524

    Article  Google Scholar 

  21. Wang Q, Li JY, Gouge M et al (2016) Physics-based multivariable modeling and feedback linearization control of melt-pool geometry and temperature in directed energy deposition. ASME J Manuf Sci Eng 139(2):021013. https://doi.org/10.1115/1.4034304

    Article  Google Scholar 

  22. Weglowski MS (2007) Investigation on the electric arc light emission in TIG welding. Int J Comput Mat Sci Surface Eng 1(6):734–749

    Google Scholar 

  23. Zhao CX, Richardson IM, Kenjeres S et al (2009) A stereo vision method for tracking particle flow on the weld pool surface. J Appl Phys 105(12):2570–3147

    Article  Google Scholar 

  24. Zhang Z (2000) A flexible new technique for camera calibration. IEEE T Pattern Anal 22(11):1330–1334

    Article  Google Scholar 

  25. Bin Z, Sen C, Wang Ju et al (2019) Feature points extraction of laser vision weld seam based on genetic algorithm. Chin J Lasers 46(1):0102001. https://doi.org/10.3788/CJL201946.0102001

    Article  Google Scholar 

Download references

Acknowledgments

The authors are grateful for the financial support provided by the China Scholarship Council and Basic and Applied Basic Research Foundation of Guangdong Province (Grant No. 2022A1515110733). The Cranfield University and the Ji Hua Laboratory are also gratefully acknowledged.

Author information

Authors and Affiliations

  1. Bionic Robot Research Department, Ji Hua Laboratory, Foshan, 528200, Guangdong, People’s Republic of China

    Bao-Ri Zhang

  2. Guangdong Provincial Engineering Research Center for Special Welding Technology and Equipment, School of Mechanical and Automotive Engineering, South China University of Technology, Guangzhou, 510640, People’s Republic of China

    Yong-Hua Shi

Authors
  1. Bao-Ri Zhang
    View author publications

    You can also search for this author inPubMed Google Scholar

  2. Yong-Hua Shi
    View author publications

    You can also search for this author inPubMed Google Scholar

Corresponding author

Correspondence to Yong-Hua Shi.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhang, BR., Shi, YH. A novel weld-pool-length monitoring method based on pixel analysis in plasma arc additive manufacturing. Adv. Manuf. 12, 335–348 (2024). https://doi.org/10.1007/s40436-023-00466-w

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s40436-023-00466-w

Keywords