Determination of submerged arc welding process parameters using Taguchi method and regression analysis

Abstract

This paper details the application of Taguchi technique and regression analysis to determine the optimal process parameters for submerged arc welding (SAW). The planned experiments are conducted in the semiautomatic submerged arc welding machine and the signal-to-noise ratios are computed to determine the optimum parameters. The percentage contribution of each factor is validated by analysis of variance (ANOVA) technique. Multiple regression analysis (MRA) is conducted using statistical package for social science (SPSS) software and the mathematical model is build to predict the bead geometry for any given welding conditions.