USE OF DESIRABILITY FUNCTION AND PRINCIPAL COMPONENT ANALYSIS IN GREY-TAGUCHI APPROACH TO SOLVE CORRELATED MULTI-RESPONSE OPTIMIZATION IN SUBMERGED ARC WELDING

Submerged arc welding (SAW) is an important metal fabrication technology specially applied to join metals of large thickness in a single pass. In order to obtain an efficient joint, several process parameters of SAW need to be studied and precisely selected to improve the bead quality. Many methodologies have been proposed in the past research to address this issue. However, a good number of past works seek to optimize SAW process parameters with only one response. In practical situations, not only influence of process parameters and their interactive effects on output responses are to be critically examined but also attempt is to be made to optimize more than one response simultaneously. To this end, the present study considers four process control parameters viz. voltage (OCV), wire feed rate, traverse speed and electrode stick-out. The selected weld quality characteristics related to features of bead geometry are depth of penetration, reinforcement and bead width. An integrated approach capable of overcoming the drawbacks of traditional optimization approaches has been suggested and results thereof analyzed. The proposed approach explores Orthogonal Array (OA) experiments for adaptation to grey-Taguchi method. Principal Component Analysis (PCA) has been used to convert correlated response values into uncorrelated quality indices called principal components. Based on desirability loss, grey relational coefficients of each quality indices have been calculated. These coefficients have been further accumulated to calculate overall grey relational grade. Individual response weights have been calculated using entropy measurement technique. Finally, Taguchi method has been adopted for optimization purpose. The study finally demonstrates the robustness and flexibility of the integrated approach in solving multi-criteria optimization problem in SAW process.