Abstract

Submerged Arc Welding (SAW) is a safe and efficient process for joining thick plates AISI 1020. A high-quality weld joint is a critical objective in a series of optimization studies. The current study focuses on maximizing the ultimate tensile strength and minimizing the peak temperature using Taguchi, Genetic Algorithm (GA), and Simulated Annealing (SA) algorithms. The input parameters in the three techniques were voltage (V), welding speed (S), and wire feed rate (F). At a 95% confidence level, the regression models were combined using the ANOVA to predict tensile strength and peak temperature. The maximum ultimate tensile strength was 599MPa achieved at a welding speed of 30 mm/s, an arc voltage of 30 V, and a wire feed rate of 120 mm/s, while the minimal peak temperature was 417C under the same conditions. With the increase of the welding parameters (welding speed, arc voltage, and feed rate), the ultimate tensile strength was increased. Furthermore, the average hardness achieved was 250 at welding metal and 292 at HAZ, while it was 275 at the base metal. The results were supported with an examination of microstructure. In the heat-affected zone (HAZ), the grain was finer while its grain size was larger in specimens with high tensile strength. It was noticed that the HAZ contains pearlite and some colonies of ferrite.

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