Mathematical modeling and intelligent optimization of submerged arc welding process parameters using hybrid PSO-GA evolutionary algorithms

Now-a-days, submerged arc welding processes (SAW) are immensely being applied for joining the thick plates and surfacing application. However, the selection of optimal SAW process parameters is indeed an intricate task which aims to accomplish the desired quality of welded part at an economic way. Therefore, in the present paper, the research efforts are made on an implementation of efficient hybrid intelligent algorithms, i.e., hybrid particle swarm optimization and genetic algorithm (hybrid PSO-GA) for the optimization of SAW process parameters. The emphasis was given on different direct parameters such as voltage, wire feed rate, welding speed and nozzle to plate distance and indirect parameters such as flux condition and plate thickness, respectively. The parameters were chosen at two levels using fractional factorial design to study their effect on responses including flux consumption, metal deposition rate and heat input. Besides, the linear regression technique and analysis of variance were used for mathematical modeling of each response. Then, the direct effect and interaction effect on selected responses were investigated by 3D surface plots. At the end, the performance of hybrid PSO-GA is compared with general PSO and GA algorithms for indices including success rate, best solution, mean, computational time, standard deviation and mean absolute percentage error between. The overall results suggested that the hybrid PSO-GA is better option than other two algorithms, i.e., PSO and GA for obtaining the optimum SAW process parameters.