Optimization of MRR and Surface Roughness in PAC of EN 31 Steel Using Weighted Principal Component Analysis

Abstract

Abstract In this paper, an attempt has been made to optimize the process parameters for multi-responses (material removal rate, MRR and surface roughness) in plasma arc cutting (PAC) of EN 31 steel using weighted principal component analysis (WPCA). For surface roughness characteristics, five different surface roughness parameters (centre line average roughness: R a , root mean square: R q , skewness: R sk , kurtosis: R ku and mean line peak spacing: R sm ) are considered. Three process parameters viz. gas pressure, arc current and torch height are considered. The experimental plan is based on Taguchi L 27 orthogonal array (OA). To convert the multi-responses problem to a single response optimization problem, WPCA is applied to compute a multi-response performance index (MPI) and then MPI has been optimized using Taguchi method. The optimum combination of process parameters has been found for maximum MRR and minimum surface roughness and verified through a confirmation test. Also, ANOVA is carried out and it is seen that the gas pressure is the most significant factor followed by arc current.