The optimization of machining processes is a deciding factor when increasing productivity and ensuring product quality. The response characteristics, such as surface roughness, material removal rate, tool wear, and cutting time, of the finish turning process have been simultaneously optimized. We used the Taguchi-based design of experiments L9(3⁴) in this study to test and find the best values for process parameters like cutting speed, feed rate, depth of cut, and nose radius. The Taguchi-based multi-objective grey relational approach (GRA) method was used to address the turning problem of Inconel 718 alloy to increase productivity, i.e., by simultaneously minimizing surface roughness, tool wear, and machining time. GRA and the S/N ratio derived from the Taguchi approach were utilized to combine many response characteristics into a single response. The grey relational grade (GRG) produces results such as estimations of the optimal level of input parameters and their proportional significance to specific quality characteristics. By employing ANOVA, the significance of parameters with respect to individual responsibility and the overall quality characteristics of the cutting process were ascertained. The single-objective optimization yielded the following results: minimal surface roughness of 0.167 µm, tool wear of 44.65 µm, minimum cutting time of 19.72 s, and maximum material speed of 4550 mm³/min. While simultaneously optimizing the Inconel 718 superalloy at a cutting speed of 100 m/min, depth of cut of 0.4 mm, feed rate of 0.051 mm/rev, and tool nose radius of 0.4 mm, the results of the multi-objective optimization showed that all investigated response characteristics reached their optimal values (minimum/maximum). To validate the results, confirmatory experiments with the most favorable outcomes were conducted and yielded a high degree of concurrence.

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.

The majority of work in cutting machining processes is focused on choosing the parameters that will result in the greatest rate of material removal and the least amount of surface roughness, cutting temperatures, cutting pressures, vibrations, etc., which are the main quality responses. Surface roughness is one of the most precise quality criteria that affects how machined parts work. By choosing the appropriate cutting settings, it should be possible to get a greater surface finish and a longer service life for the machined parts. The link between changes in surface roughness caused by turning operations with respect to different machining settings is investigated in this study using the Taguchi method L9(33). The orthogonal array, signal-to-noise ratio, and analysis of variance are utilized to examine the performance characteristics when turning Inconel 718 bars with CCMT09T308N-SU coated cemented carbide insert tools. The optimal setting of cutting parameters to lessen surface roughness is chosen using the Taguchi method. Speed, feed rate, and cut depth are the three cutting parameters. Experimental results are given to illustrate the effectiveness of this strategy.