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Gas tungsten arc welding (GTAW) technology is widely used in industry and has advantages, including high precision, excellent welding quality, and low equipment cost. However, the inclusion of a large number of process parameters hinders its application on a wider scale. Therefore, there is a need to implement the prediction and optimization models that effectively enhance the process performance of the GTAW process in different applications. In this study, a five-factor five-level central composite design (CCD) matrix was used to conduct GTAW experiments. AISI 1020 steel blank was used as a substrate; UTP AF Ledurit 60 and UTP AF Ledurit 68 were used as the materials of two tubular wires. Further, an artificial neural network (ANN) was used to simulate the GTAW process and then combined with a genetic algorithm (GA) to determine welding parameters that can provide an optimal weld. In welding experiments, five different welding current levels, welding speed, distance to the nozzle, angle of movement, and frequency of the wire feed pulses were used. Using GA, optimal welding parameters were determined: welding current = 222 A, welding speed = 25 cm/min, nozzle deflection distance = 8 mm, travel angle = 25°, wire feed pulse frequency = 8 Hz. The determination coefficient (R2) and RMSE value of all response parameters are satisfactory, and the R2 of all the data remained higher than 0.65.

Investigations on quality characteristics in gas tungsten arc welding process using artificial neural network integrated with genetic algorithm