Generally, the quality of a weld joint is directly influenced by the welding input parameter settings. In this study, the regression modeling is used in order to establish the relationships between input and output parameters for Gas Metal Arc Welding (GMAW) process. To gather the required data for modeling, actual tests were carried out based on the proposed Taguchi experimental matrix design. The process variables considered here include voltage (V); wire feed rate (F); torch Angle (A); welding speed (S) and nozzle-to-plate distance (D). The process output characteristics include weld bead height, width and penetration. To develop mathematical models, various regression functions have been fitted on the experimental data. The adequacies of the models are then evaluated using analysis of variance (ANOVA) technique. The best and most fitted model is then selected based on the ANOVA results and other statistical analysis. The ANOVA results recommend that the curvilinear model is the best fit in this case. In the next stage, the selected model is implanted into a Simulated Annealing (SA) optimization algorithm. This optimization procedure has been developed in order to determine the best set of process variables levels for any desired weld bead geometry characteristics. Computational results show very good compatibility with experimental data and demonstrate the effectiveness of the proposed modeling and optimization approach.

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