Comparing DEA and principal component analysis in the multiobjective optimization of P-GMAW process

Abstract

The optimization of a multiresponse manu- facturing process is not a trivial task. Many authors have tried to overcome the particular difficulties observed in this knowledge area exploring the powerful mechanisms present in a great deal of techniques like design of experi- ments, response surface methodology (RSM), principal component analysis (PCA) and mathematical program- ming. In this sense, this paper presents an alternative hybrid approach, combining RSM and data envelopment analysis (DEA), a popular linear programming technique useful to compare efficiency of decision making units. The basic idea is to optimize a set of multiple correlated responses of a well-defined manufacturing process using DEA as an algorithm for generated the singular objective function. This alternative proposal is compared to multivar- iate response surface methodology, a stochastic approach based on the PCA, a multivariate statistical technique usu- ally employed with Taguchi multiresponse designs. Since a great number of manufacturing processes present sets of multiple correlated responses, a case study based in a five quality characteristics of a pulsed GMAW welding process