Parametric modeling and optimization of novel water-cooled advanced submerged arc welding process
- 62 Downloads
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.
KeywordsFlux consumption Torch ASAW Metal deposition rate Heat input Multi-objective optimization Jaya algorithm
Unable to display preview. Download preview PDF.
- 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. https://doi.org/10.1007/s00170-006-0917-4 Google Scholar
- 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.Murayama M, OAZAMOTO D, OOE K (2015) Narrow gap gas metal arc (GMA) welding technologies. JFE Technical Report 20:147–153Google Scholar
- 13.Pandey S (2004) Welding current and melting rate in submerged arc welding: a new approach. Australasian. Weld J 49:33–42Google Scholar
- 14.Shukla DK, Pandey S (2012) Dilution control by advanced submerged arc welding. Adv Mater Res 488-489:1737–1741. https://doi.org/10.4028/www.scientific.net/AMR.488-489.1737 CrossRefGoogle Scholar
- 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
- 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. https://doi.org/10.5267/j.ijiec.2015.8.004
- 21.Rao RV, Rai DP, Balic J (2016) Surface grinding process optimization using Jaya algorithm. 411:487–495. https://doi.org/10.1007/978-81-322-2731-1_46
- 23.Anderson MJ, Whitcomb PJ (2016) DOE simplified: practical tools for effective experimentation. CRC PressGoogle Scholar
- 24.Montgomery DC (2001) Design and analysis of experiments, John Wiley & Sons. New York:64–65Google Scholar