Optimization of Arc Welding Process Using a Genetic Algorithm Parameters

The purpose of this study is to propose a method to decide nearoptimal settings of the welding process parameters using a genetic algorithm. This method tries to find the near-optimal settings of the welding process parameters through experiments without a model between input and output variables. It has the advantage of being able to carry out searches without modifying the design space, which includes some irregular points. The method suggested in this study was used to determine the welding process parameters by which the desired weld bead geometry was formed in gas metal arc (GMA) welding. The output variables were the bead height and depth of penetration of the weld bead. These output variables were determined according to the input variables, which are the root opening, wire feed rate, welding voltage and welding speed. The number of levels for the input variables was 4, 16, 16 and 16, respectively, and the total search points were 16,384. This study describes how to obtain near-optimal welding conditions over a wide search space, conducting a relatively small number of experiments.