An Experimental Analysis of Taguchi-Based Gray Relational Analysis, Weighted Gray Relational Analysis, and Data Envelopment Analysis Ranking Method Multi-Criteria Decision-Making Approaches to Multiple-Quality Characteristic Optimization in the CNC Drilling Process

Abstract

The goal of this research is to optimize the input parameters utilized in dry CNC drilling of forging steel to attain sustainable machining. Particular emphasis will be placed on achieving high productivity while minimizing the impact on surface quality. To achieve the aforementioned goal, three Taguchi-based multi-criteria decision-making (MCDM) approaches, such as traditional gray relational analysis (GRA), weighted gray relational analysis (WGRA), and data envelopment analysis ranking (DEAR), were used for simultaneous optimization of the MRR and Ra. In Taguchi’s L12 (24) orthogonal array design, the cutting mode parameters—such as cutting speed, depth of cut, feed rate, and point angle—have been chosen as the input parameters for the modeling and analysis of the drilling process characteristics. The process of determining the effect of the input parameters on the output parameters was carried out with the use of analysis of variance (ANOVA). The best results from the studies were Ra = 2.19 and MRR = 375 mm3/s, which corresponded to Taguchi’s single optimization levels, S2F1D1A2 and S2F2D2A1, respectively. In the next step, the performance values obtained for each MCDM technique were reoptimized using the Taguchi method, and the optimal levels were obtained: for traditional GRA, the level S2F1D2A1 (Ra = 2.52 µm, MRR = 125 mm3/s); for WGRA, the level S2F1D1A1 (Ra = 2.31 µm, MRR = 83 mm3/s); and for DEAR, the level S2F2D2A1 (Ra = 4.42 µm, MRR = 375 mm3/s), respectively. Lastly, in order to compare the experiments’ performance, validation tests were carried out. The results of the experiments using multi-objective optimization show that traditional GRA improved the overall quality response characteristics by 29.86% compared to the initial setup parameters, while weighted GRA improved them by 34.48%, with the DEAR method providing an improvement of 96%. Based on the findings of this investigation, the DEAR optimization method outperforms the GRA method. As a result, the proposed methods are useful tools for multi-objective optimization of cutting parameters.