In the present scenario, Industries are finding ways to increase productivity and reduce cost by implementing new technologies like machine vision to all automated fields. This is a study about the enhancement and/or the application of vision power in the field of robot welding and how it controls variable parameters to optimize its production.

This study is aimed at obtaining a relationship between the values of dependent and independent variables of robotic gas metal arc welding, using linear regression techniques. The welding parameters such as the arc current, stick out, arc voltage and welding speed are taken as independent variables and bead geometry namely bead width, bead penetration and bead reinforcement were dependent variables. In this study, the important factors on bead geometry are considered and the other parameters are held as constant. An extension of this model, namely multiple linear regressions, is used to represent the relationship between a dependent variable and several independent variables. Taguchi's L 27 , 3 level 4 parameter orthogonal array design of experiments, twenty seven samples were analyzed and the bead geometry values of the weld bead have been measured. Then, the relationship between the welding parameters is modeled and expressed in multiple linear regression models by a script approach with MATLAB. Each model is checked for its adequacy by using ANOVA. The proposed model is compared with the experiment values and it has positive correlation with maximum of 3.67% error. The results were confirmed by further experiments.

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