Application of PCA-based hybrid Taguchi method for correlated multicriteria optimization of submerged arc weld: a case study

Abstract

In the present work, an attempt has been made to solve the correlated multiple criteria optimization problem of submerged arc welding. The target was to search an optimal process environment, capable of producing desired bead geometry parameters of the weldment. Four correlated features of bead geometry: depth of penetration, reinforcement, bead width, and percentage dilution have been selected in the study. The process environment has been assumed consisting of four variables like voltage (OCV), wire feed rate, traverse speed, and electrode stick-out. Multiple correlated responses have been converted into independent quality indices called principal components. Principal component analysis (PCA) has been adapted to covert multiple objectives of the optimization problem into a single objective function. This single objective function has been denoted as composite principal component. Taguchi’s robust optimization technique has been applied to determine the optimal setting, which can maximize the composite principal component. Result of this aforesaid optimization technique has been compared to that of grey-Taguchi technique; another approach which is widely used for solving multicriteria optimization problems. A confirmatory test showed satisfactory result. This concludes application feasibility of PCA-based hybrid Taguchi method for parametric optimization of SA weld.