Parametric modeling and optimization of novel water-cooled advanced submerged arc welding process

  • Ankush Choudhary
  • Manoj Kumar
  • Deepak Rajendra Unune


In this research, a novel water-cooled torch is developed for continuous advanced submerged arc welding (ASAW) operation to enhance metal deposition rate at reduced heat input. Initially, the power saving and metal deposition rate attained by use of the developed torch in ASAW have been compared with submerged arc welding to demonstrate the better efficiency of the developed torch. Then, an experimental investigation has been performed based on central composite design of response surface methodology to study the effect of process parameters, viz., welding voltage, wire feed rate, welding speed, nozzle to plate distance, and preheat current on ASAW characteristics, namely, flux consumption, metal deposition rate, and heat input. The relationships between process parameters and response parameters have been established. Finally, the Jaya algorithm technique has been used for multi-objective optimization of process parameters to achieve better welding performance.


Flux consumption Torch ASAW Metal deposition rate Heat input Multi-objective optimization Jaya algorithm 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Tušek J (2004) Mathematical modelling of melting rate in arc welding with a triple-wire electrode. J Mater Process Technol 146(3):415–423. CrossRefGoogle Scholar
  2. 2.
    Ishigami A, Roy MJ, Walsh JN, Withers PJ (2016) The effect of the weld fusion zone shape on residual stress in submerged arc welding. Int J Adv Manuf Technol 90(9–12):3451–3464. Google Scholar
  3. 3.
    Mohammadijoo M, Collins L, Henein H, Ivey DG (2017) Evaluation of cold wire addition effect on heat input and productivity of tandem submerged arc welding for low-carbon microalloyed steels. Int J Adv Manuf Technol 92:817–829. CrossRefGoogle Scholar
  4. 4.
    Rao RV, Kalyankar VD (2013) Experimental investigation on submerged arc welding of Cr–Mo–V steel. Int J Adv Manuf Technol 69(1–4):93–106. CrossRefGoogle Scholar
  5. 5.
    Prasad K, Dwivedi DK (2008) Microstructure and tensile properties of submerged arc welded 1.25Cr-0.5Mo steel joints. Mater Manuf Process 23(5):463–468. CrossRefGoogle Scholar
  6. 6.
    Datta S, Bandyopadhyay A, Kumar Pal P (2007) Modeling and optimization of features of bead geometry including percentage dilution in submerged arc welding using mixture of fresh flux and fused slag. Int J Adv Manuf Technol 36(11–12):1080–1090. Google Scholar
  7. 7.
    Wang LL, Wei HL, Xue JX, DebRoy T (2018) Special features of double pulsed gas metal arc welding. J Mater Process Technol 251:369–375. CrossRefGoogle Scholar
  8. 8.
    Kozuki S, Hayakawa N, Oi K (2015) Multiple-electrode submerged arc welding process with low heat input. JFE Technical Report 20:106–111Google Scholar
  9. 9.
    Murayama M, OAZAMOTO D, OOE K (2015) Narrow gap gas metal arc (GMA) welding technologies. JFE Technical Report 20:147–153Google Scholar
  10. 10.
    Ahsan MRU, Kim YR, Kim CH, Kim JW, Ashiri R, Park YD (2016) Porosity formation mechanisms in cold metal transfer (CMT) gas metal arc welding (GMAW) of zinc coated steels. Sci Technol Weld Join 21(3):209–215. CrossRefGoogle Scholar
  11. 11.
    Arif N, Chung H (2015) Alternating current-gas metal arc welding for application to thick plates. J Mater Process Technol 222:75–83. CrossRefGoogle Scholar
  12. 12.
    Lu Y, Chen S, Shi Y, Li X, Chen J, Kvidahl L, Zhang YM (2014) Double-electrode arc welding process: principle, variants, control and developments. J Manuf Process 16(1):93–108CrossRefGoogle Scholar
  13. 13.
    Pandey S (2004) Welding current and melting rate in submerged arc welding: a new approach. Australasian. Weld J 49:33–42Google Scholar
  14. 14.
    Shukla DK, Pandey S (2012) Dilution control by advanced submerged arc welding. Adv Mater Res 488-489:1737–1741. CrossRefGoogle Scholar
  15. 15.
    Om H, Pandey S (2014) Establishing relationship between ASAW parameters and welding voltage during surfacing. In: 4th Int. conference on Advances in mechanical, Material, Manufacturing, Automobile, Aeronautical Engineering and Applied Physics (AMAEAP-2014), JNU, New DelhiGoogle Scholar
  16. 16.
    Jiang P, Cao L, Zhou Q, Gao Z, Rong Y, Shao X (2016) Optimization of welding process parameters by combining Kriging surrogate with particle swarm optimization algorithm. Int J Adv Manuf Technol 86(9–12):2473–2483. CrossRefGoogle Scholar
  17. 17.
    Shao Q, Xu T, Yoshino T, Song N (2017) Multi-objective optimization of gas metal arc welding parameters and sequences for low-carbon steel (Q345D) T-joints. J Iron Steel Res Int 24(5):544–555. CrossRefGoogle Scholar
  18. 18.
    Chandrasekhar N, Ragavendran M, Ravikumar R, Vasudevan M, Murugan S (2017) Optimization of hybrid laser–TIG welding of 316LN stainless steel using genetic algorithm. Mater Manuf Process 32(10):1094–1100. CrossRefGoogle Scholar
  19. 19.
    Venkata Rao R (2016) Jaya: a simple and new optimization algorithm for solving constrained and unconstrained optimization problems. Int J Ind Eng Comput:19–34.
  20. 20.
    Tarng YS, Yang WH, Juang SC (2000) The use of fuzzy logic in the Taguchi method for the optimisation of the submerged arc welding process. Int J Adv Manuf Technol 1(16-9):688–694. CrossRefGoogle Scholar
  21. 21.
    Rao RV, Rai DP, Balic J (2016) Surface grinding process optimization using Jaya algorithm. 411:487–495.
  22. 22.
    Shen S, Oguocha INA, Yannacopoulos S (2012) Effect of heat input on weld bead geometry of submerged arc welded ASTM A709 grade 50 steel joints. J Mater Process Technol 212(1):286–294. CrossRefGoogle Scholar
  23. 23.
    Anderson MJ, Whitcomb PJ (2016) DOE simplified: practical tools for effective experimentation. CRC PressGoogle Scholar
  24. 24.
    Montgomery DC (2001) Design and analysis of experiments, John Wiley & Sons. New York:64–65Google Scholar
  25. 25.
    Rao RV, Rai DP (2017) Optimization of submerged arc welding process parameters using quasi-oppositional based Jaya algorithm. J Mech Sci Technol 31(5):2513–2522. CrossRefGoogle Scholar
  26. 26.
    Rao RV, Rai DP, Balic J (2017) A multi-objective algorithm for optimization of modern machining processes. Eng Appl Artif Intell 61:103–125. CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2018

Authors and Affiliations

  • Ankush Choudhary
    • 1
  • Manoj Kumar
    • 1
  • Deepak Rajendra Unune
    • 1
  1. 1.Department of Mechanical-Mechatronics EngineeringThe LNM Institute of Information TechnologyJaipurIndia

Personalised recommendations