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The welding parameters directly affect the weld forming and the joint performance in GMA (Gas Metal Arc) welding. Because of the many parameters involved in the automatic arc welding process, it is often not realistic to use traditional experimental methods such as full factorial design. Therefore, it is important to find the good experimental design method for determining the welding parameters that obtain an optimal joint quality with a minimal number of experiments. Therefore, this study is aimed at investigating the effect of DOE (Design of Experiment) methods on bead width of mild steel parts welded by the automatic GAM welding process. Taguchi method was employed to study effect the welding parameters and optimization of bead width, while Box-Behnken method was utilized to develop a mathematical model relating the bead width to welding parameters such as welding voltage, arc current, welding speed and CTWD (Contact Tip to Work Distance). The S/N (signal-to-noise) ratio and the ANOVA (Analysis of Variance) were employed to find the optimal bead width in automatic GAM welding process. Confirmation tests were carried out illustrating the effectiveness of the Taguchi method. The results showed that welding current mainly affected the bead width. The predicted bead width of 3.12mm was in agreement with the confirmation tests. With the regression coefficient analysis in the Box-Behnken design, a relationship between bead width and welding parameters was obtained. A second-order empirical model has also been established between the welding parameters and the bead width as welding quality. The developed model is adequate to navigate the design space.

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The automatic welding system is presently made use of high volume production industries even if the cost of the related equipment is justified by the large number of pieces to be made. Also, the detailed movement devices with the predetermined sequences of welding parameter and the use of timers to form the weld joints were required. A new mathematical model that predict the optimal welding parameters on a given bead geometry and accomplish the desired mechanical properties of the weldment to make the automatic GMA (Gas Metal Arc) welding process should be needed. The developed model should be employed a wide range of material thicknesses and be applicable for all welding positions as well. In addition, the algorithm must be available in the form of mathematical equations which can be programmed easily to the robot and give a high degree of confidence in predicting the bead dimensions. In this study, two regression models with global regression and cluster-wise regression are proposed to be applicable for prediction of optimal welding parameters on the bead reinforcement area. For development of the proposed regression models, an attempt has been done for applying to a several methods. A series experiments to research the effects of welding parameters on bead reinforcement area as a function of key output parameters for the lab-joint weld in the automatic GMA welding process was performed. Not only the fitting of these models was checked and compared by using a variance test (ANOVA), but also the prediction of bead reinforcement area using the developed regression models were carried out the basis of the additional experiments.