Multi-objective optimization of pulsed gas metal arc welding process based on weighted principal component scores

Abstract

Most welding processes present large sets of correlated quality characteristics. With this particularity in mind, we present a multi-objective optimization technique based on Principal Component Analysis (PCA) and response surface methodology (RSM). This two-fold technique utilizes PCA to factorize the original welding responses. The original responses—obtained through a Central Composite Design—are then replaced by the resulting principal component scores. The technique’s advantage is that it reduces the data set and still considers the correlation among the responses. Quite often, however, the first principal component alone cannot explain the amount of variance–covariance structure of the welding responses. In this paper, we remedy this shortfall by proposing an objective function established in terms of the most significative principal component scores (weighted by their respective eigenvalues). Experimental results were obtained with a multiresponse pulsed gas metal arc welding process. These results, when compared with other strategies of multiresponse combination, verify the adequacy of our proposed approach.