Optimization of Control Parameters in Submerged Arc Welding Using GA

ABSTRACT- Submerged Arc Welding (SAW) is one of the most important metal joining processes in metal forming industries. In this technique, optimal selection of process parameters is vital in achieving high quality joints. In this paper, modeling and optimization of process parameters in SAW have been addressed. To establish the mathematical relationships between important process-control variables and the bead-quality specifications, regression analysis based on a five-level factorial technique is employed. The adequacy of the developed models is also verified using ANOVA method. The proposed models are then implanted into a Genetic Algorithm (GA) to determine the best SAW process parameters for any target values of weld bead geometries. The computational results demonstrate that GA method is quite effective in predicting process parameters for any desired weld bead geometry