Multi-variable statistical models for predicting bead geometry in gas metal arc welding

  • Rahul Ram ChandrasekaranEmail author
  • Michael J. Benoit
  • Jeff M. Barrett
  • Adrian P. Gerlich


Statistical models were developed to study the effect of gas metal arc welding process parameters (i.e., wire feed speed, voltage, travel speed, and shielding gas chemistry) on the resultant weld bead width, penetration, and reinforcement height, using a factorial design of experiment. Analysis of variance (ANOVA) indicated that the weld width depended on voltage, travel speed, gas type, and the interactions between these factors. The weld penetration depended only on wire feed speed and gas type, as well as the two-way interactions of wire feed speed with travel speed and gas type. Reinforcement height depended on travel speed, wire feed speed, and their two-way interactions with gas type. Residual analysis revealed that all assumptions inherent in the regression analysis were satisfied over the range of welding parameters considered in this study. The predictive power of the statistical models was validated using intermediate process parameter values in the experimental design, and it was found that predicted values were mainly in agreement with the measured values for a 95% prediction interval.


Gas metal arc welding (GMAW) Weld bead geometry Design of experiment (DOE) Analysis of variance (ANOVA) Shielding gas 



Weld bead width


Weld penetration (depth)


Reinforcement height


Travel speed




Wire feed speed


Gas type


Statistical model coefficients


Factor levels


Orthogonally coded x value


Regression coefficient (coefficient of determination)


Adjusted regression coefficient


Predicted regression coefficient


Argon (shielding) gas


Carbon dioxide (shielding) gas


Gas metal arc welding


Analysis of variance


Design of experiments


Shielded metal arc welding


Metal cored arc welding


Flux cored arc welding


Submerged arc welding


Gas tungsten arc welding


Direct current electrode positive


Constant voltage




Funding information

The authors received financial support from the Natural Science and Engineering Research Council of Canada (NSERC), and the Canadian Welding Bureau (CWB) Foundation.


  1. 1.
    Baba H, Era T, Ueyama T, Tanaka M (2017) Single pass full penetration joining for heavy plate steel using high current GMA process. Weld World 61(5):963–969. CrossRefGoogle Scholar
  2. 2.
    Perić M, Garašić I, Tonković Z, Vuherer T, Nižetić S, Dedić-Jandrek H (2019) Numerical prediction and experimental validation of temperature and residual stress distributions in buried-arc welded thick plates. Int J Energy Res 43:3590–3600. CrossRefGoogle Scholar
  3. 3.
    Zhang W, Kim CH, DebRoy T (2004) Heat and fluid flow in complex joints during gas metal arc welding—part I: numerical model of fillet welding. J Appl Phys 95(9):5210–5219. CrossRefGoogle Scholar
  4. 4.
    Fan HG, Kovacevic R (2004) A unified model of transport phenomena in gas metal arc welding including electrode, arc plasma and molten pool. J Phys D: Appl Phys 37(18):2531. CrossRefGoogle Scholar
  5. 5.
    Mendez PF, Ramirez MA, Trapaga G, Eagar TW (2002) Scaling Laws in the Welding Arc, Mathematical Modelling of Weld Phenomena 6. Institute of Materials, LondonGoogle Scholar
  6. 6.
    Gunaraj V, Murugan N (2000) Prediction and optimization of weld bead volume for the submerged arc process—part 1. Weld J 78:286s–294sGoogle Scholar
  7. 7.
    Datta S, Bandyopadhyay A, Pal PK (2008) Grey-based taguchi method for optimization of bead geometry in submerged arc bead-on-plate welding. Int J Adv Manuf Technol 39:1136. CrossRefGoogle Scholar
  8. 8.
    Tarng YS, Yang WH (1998) Optimisation of the weld bead geometry in gas tungsten arc welding by the Taguchi method. Int J Adv Manuf Technol 14:549–554. CrossRefGoogle Scholar
  9. 9.
    Gunaraj V, Murugan N (1999) Application of response surface methodology for predicting weld bead quality in submerged arc welding of pipes. J Mater Process Technol 88(1-3):266–275. CrossRefGoogle Scholar
  10. 10.
    Nagesh DS, Datta GL (2002) Prediction of weld bead geometry and penetration in shielded metal-arc welding using artificial neural networks. J Mater Process Technol 123(2):303–312. CrossRefGoogle Scholar
  11. 11.
    Kanti KM, Rao PS (2008) Prediction of bead geometry in pulsed GMA welding using back propagation neural network. J Mater Process Technol 200(1-3):300–305CrossRefGoogle Scholar
  12. 12.
    Podržaj P (2018) An overview of arc welding control systems. doi
  13. 13.
    Murugan N, Parmar RS (1994) Effects of MIG process parameters on the geometry of the bead in the automatic surfacing of stainless steel. J Mater Process Technol 41:381–398. CrossRefGoogle Scholar
  14. 14.
    Weman K (2003) Welding Processes Handbook 2nd Edition. Woodhead Publishing Ltd, pp 44–46Google Scholar
  15. 15.
    Karadeniz E, Ozsarac U, Yildiz C (2007) The effect of process parameters on penetration in gas metal arc welding processes. Mater Des 28:649–656. CrossRefGoogle Scholar
  16. 16.
    Murugan N, Gunaraj V (2005) Prediction and control of weld bead geometry and shape relationships in submerged arc welding of pipes. J Mater Process Technol 168:478–487. CrossRefGoogle Scholar
  17. 17.
    R Core Team (2017) “R: A Language and Environment for Statistical Computing”. Accessed 20 Dec 2018
  18. 18.
    RStudio Team (2015). RStudio: Integrated Development for R. RStudio, Inc., Boston, MA URL Accessed 20 Dec 2017
  19. 19.
    Bauccio M (Ed.) (1993) ASM metals reference book, ASM internationalGoogle Scholar
  20. 20.
    Block-Bolten A, Eagar TW (1984) Metal vaporization from weld pools. Metall. Trans. B 15(3):461–469. CrossRefGoogle Scholar
  21. 21.
    Montgomery DC (2012) Design and analysis of experiments, 8th edn. Wiley, HobokenGoogle Scholar
  22. 22.
    Kou S (2003) Welding Metallurgy Second Edition. John Wiley & Sons IncGoogle Scholar
  23. 23.
    Wang LL, Lu FG, Wang HP, Murphy AB, Tang XH (2014) Effects of shielding gas composition on arc profile and molten pool dynamics in gas metal arc welding of steels. J Phys D: Appl Phys 47:465202. CrossRefGoogle Scholar
  24. 24.
    Lancaster JF (1986) The Physics of Welding 2nd Edition. International Institute of WeldingGoogle Scholar
  25. 25.
    Tsai MC, Kou S (1990) Electromagnetic force induced convection in weld pools with a free surface. Weld J Res Suppl 241-246sGoogle Scholar
  26. 26.
    Murray PE, Scotti A (2013) Depth of penetration in gas metal arc welding. Sci Technol Weld Joining: pg 112-117. doi CrossRefGoogle Scholar
  27. 27.
    Kim YS, Eager TW (1993) Analysis of metal transfer in gas metal arc welding. Weld J 72(6):1143–1152Google Scholar
  28. 28.
    Lu S, Fujii H, Nogi K (2004) Marangoni convection in weld pool in CO 2-Ar-shielded gas thermal arc welding. Metall Mater Trans A 35(9):2861–2867. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Rahul Ram Chandrasekaran
    • 1
    Email author
  • Michael J. Benoit
    • 1
  • Jeff M. Barrett
    • 2
  • Adrian P. Gerlich
    • 1
  1. 1.Department of Mechanical and Mechatronics EngineeringUniversity of WaterlooWaterlooCanada
  2. 2.Department of KinesiologyUniversity of WaterlooWaterlooCanada

Personalised recommendations