Parametric Optimization of TIG welding on UNS S31603 using Genetic Algorithm


  • Prasad Mehulkumar Anil P.G. Student, Merchant Engineering College
  • Nirav B. Patel Assistant Professor, Merchant Engineering College


TIG welding, Genetic algorithm, Aspect ratio, regression, ANOVA.


—Tungsten Inert Gas (TIG) welding with filler wire addition is a candidateprocess for welding of UNS
S31603/316L austenitic stainless steel. In GTAW, the qualityof the weld is characterized by the weld-bead geometry as it
influences the mechanicalproperties and its performance during service. This work focuses on the development
ofregression model and optimization using Genetic Algorithm for determining the optimum/near-optimum TIGprocess
parameters for obtaining the optimum weld-bead geometry during welding of 316Lstainless steel. Parameters selected
for study were Welding current, Shielding Gas Flow, Filler Rod Diameter and the response selected was Aspect Ratio.
Using the experimental data generated on the influence of process variableson weld -bead geometry, regression models
correlating the weld-bead shape parameterswith the process parameters has been developed using Regression Method .
ANOVA Analysis was done to obtain the significant parameters. Percent contribution of various parameter found was
Shielding gas flow 43.52%, Filler rod diameter 31.09%, Welding current 22.13%. Experimental validation of the model
was also performed. The optimum result obtained from Genetic algorithm was welding current 80A, Shielding gas
8l/min, Filler rod diameter 1.6mm and Aspect ratio of 2.228.



How to Cite

Prasad Mehulkumar Anil, & Nirav B. Patel. (2015). Parametric Optimization of TIG welding on UNS S31603 using Genetic Algorithm. International Journal of Advance Engineering and Research Development (IJAERD), 2(5), 403–409. Retrieved from