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Determination of Optimal Pulse Metal Inert Gas Welding Parameters with a Neuro-GA Technique

Optimization of a manufacturing process is a rigorous task because it has to take into account all the factors that influence the product quality and productivity. Welding is a multi-variable process, which is influenced by a lot of process uncertainties. Therefore, the optimization of welding process parameters is considerably complex. Advancement in computational methods, evolutionary algorithms, and multiobjective optimization methods create ever-more effective solutions to this problem. This work concerns the selection of optimal parameters setting of pulsed metal inert gas welding (PMIGW) process for any desired output parameters setting. Six process parameters, namely pulse voltage, background voltage, pulse frequency, pulse duty factor, wire feed rate and table feed rate were used as input variables, and the strength of the welded plate, weld bead geometry, transverse shrinkage, angular distortion and deposition efficiency were considered as the output variables. Artificial neural network (ANN) models were used for mapping input and output parameters. Neuro genetic algorithm (Neuro-GA) technique was used to determine the optimal PMIGW process parameters. Experimental result shows that the designed parameter setting of PMIGW process, which was obtained from Neuro-GA optimization, indeed produced the desired weld-quality.