Integrated Taguchi and Grey Relational Optimization of Drilling Process Parameters

Abstract

The appropriate selection of machining parameters plays a vital role in enhancing productivity, improving surface finish, and reducing machining costs in drilling operations. The present study focuses on optimizing spindle speed, feed rate, depth of cut, and drill diameter to improve Material Removal Rate (MRR) and minimize surface roughness. Experimental investigations were conducted on EN 36 steel using a CNC drilling machine equipped with carbide-coated HSS twist drills, following an L16 orthogonal array based on the Taguchi design. Signal-to-Noise (S/N) ratio analysis was employed to identify the optimal parameter settings for the individual performance characteristics. Analysis of Variance (ANOVA) was subsequently performed to assess the statistical significance and relative contributions of the selected machining parameters. To achieve simultaneous optimization of MRR and surface roughness, Grey Relational Analysis (GRA) was implemented to convert the multiple-response problem into a single Grey Relational Grade (GRG). Based on the GRG analysis, the optimal machining conditions were determined as a spindle speed of 2500 rpm, a feed rate of 100 mm/min, a depth of cut of 15 mm, and a drill diameter of 18 mm. The ANOVA findings indicated that depth of cut was the most significant factor influencing the overall drilling performance, followed by drill diameter, spindle speed, and feed rate. The results demonstrate the applicability of integrating Taguchi methodology, ANOVA, and GRA for optimizing multiple performance characteristics in the drilling of EN 36 steel.