The aspect-based Taguchi optimization approaches have been newly accepted as important routes to optimizing the turning experimental parameters. Unfortunately, due to its embryonic development, scholars have left unexplained the effects of introducing the aspect ratios on the optimal parametric setting. To correct this deficiency, this article proposes an approach to evaluating the effects of introducing aspect ratios in turning experiments, combined with direct factors, on the optimal parametric settings. To correct this deficiency, the purpose of this article is to highlight that a standard universal evaluation method is absent in optimization analysis using the Taguchi method; it proposes an approach to evaluating the effects of introducing aspect ratios in turning experiments, in combination with direct factors, on the optimal parametric settings. Using A novel method of establishing the influence of introducing aspect ratios on the optimal parametric settings is suggested using literature review, and the examination method may be a solid basis for optimal parametric setting evaluations in future undertakings of turning operational evaluations. The Inconel X750 alloy is considered in turning operations, and experimental data from the literature are used to illustrate the method. This article finds that quantifiable differences in the mean values of optimal parametric settings exist for the turning operation of Inconel X750 alloy. The study's originality is its attention to the aspect ratio analysis regarding the optimal parametric setting in a wide range of values. This article aims to initiate discussions for a universal agreement on how the influence of introducing the aspect ratios in the factor-level combination framework of the Taguchi method may be constituted. The utility of this research effort is to enhance resource distribution planning fog turning zero material.

Optimization of welding parameters is essential on austenitic stainless steel for industrial applications since they declare the best parameters compared with prioritized constraints. However, available optimization methods, such as the Taguchi method, widely used in this research domain, are weak. Their results are merely comparative and fail to particularly show the specific factor that displays the highest performance in the process. In this paper, the aim is specifically to position the parameters in order of importance and present them in a grey wolf optimization framework. The ultimate tensile strength and yield strength were optimized, and the optimization was conducted using the C++ programming code. Literature data were analyzed for austenitic stainless steel under un-notched/smooth and notched specimen conditions. Empirical models were developed for the ultimate tensile strength and yield strength, among other principal criteria of the material. For the ultimate tensile strength, the best value was obtained at the 100th iteration as 640.75. For the yield strength, the best value of 394.98 was obtained after 100 iterations. A value of 31.07 for the PE was obtained. These results are for the unnotched specimens. However, the PE, NTS, and yield strength values for the notched specimens are 16.32, 780.12, and 494.46, respectively. Based on the findings of this study and compared with other optimization methods, the optimal parameters and outputs predicted using the grey wolf optimization approach were found to produce reliable results. This shows that the grey wolf optimization approach is a good option for predicting the optimal parameters of the tungsten arc welding process by utilizing austenitic stainless steel. The usefulness of this research effort is to help process engineers to implement robust and effective cost decisions in the production of materials based on austenitic stainless steel.

Optimization of turning process parameters in minimum quantity lubrication (MQL)-assisted mode is obligatory for enhanced efficiency and product integrity. However, little attention has been paid to analyzing situations where high search precision is needed when evaluating the optimal turning process parameters. This article applies the grey wolf optimization (GWO) approach to optimize the turning of parameters AISI 4340 alloy to enhance cutting force, surface roughness and tool wear. Based on the literature data, turning was conducted with MQL-assisted CuO and Al2O3 nanofluids. The problem was formulated by mimicking six wolves in six different objective functions. The objective functions have the responses as the dependent variables and the parameters including cutting speed, feed and cutting depth as independent variables. The hunting behavior of the wolves as they encircle the prey is interpreted to the machining task optimization. It involves three hierarchically-evaluated guides- the alpha, beta and delta wolves- positioned optimally and other wolves are updated accordingly. The cutting speed, feed and cutting depth are bound in the lower and upper limits as 80 and 140m/min, 0.05 and 0.20m/m/rev and 0.1 and 0.4mm, respectively. The grey wolf optimization algorithm optimizes the parameters to yield the cutting force, surface roughness and tool wear using Al2O3 as 199.50N, -23.54mm and 0.06mm, respectively. For the CuO, the corresponding cutting force, surface roughness and tool wear, the CuO, Al2O3 and CuO nano lubricants produced the best results. However, for mass production, selective use of CuO and Al2O3 should be made. The usefulness of this research endeavor is to help process engineers to make decisions in producing low-cost components in manufacturing.

Thermal friction estimations are presently essential on steel for manufacturing applications as they predict the aggregated energy required for the required process. However, the current thermal friction estimates are inaccurate as they exclude the optimized thresholds of both the input and output quantities. In this article, the optimization of the drilling operation process is accounted for by introducing a new method of combined Taguchi-Pareto–grey wolf-desirability function analysis applied on the AISI 304 stainless steel. An objective function was formulated using the delta values developed from the average signal-to-noise into the response table of the Taguchi method. Besides, the ranks of the parameters through the response table are taken in the reciprocal mode to evaluate the values of the linear program formulated according to the objective function and some constraints taken from the system. Six input parameters were considered tool cylindrical region diameter, friction angle, friction contact area ratio, mouthpiece thickness, feed rate and reciprocal speed. The outputs are the axial force, radial force, hole diameter dimensional error, roundness error and bushing length. These inputs and outputs were analyzed for the optimization process. Based on the results, which were solved using the C++ software, the best value converges in iteration 8 with the starting value of 1699.2. Iteration 1 drops to 11016.3 in six iterations (iterations 2 to 7) and finally converges at 11015.9 in iterations 8 through 20. The usefulness of the effort is to help process engineers to execute cost-effective energy conservation decisions in optimization that could be obtained using optimized thermal friction values.

Abrasive waterjet machining (AWJM), a known metal cutting process in manufacturing, is likely to be improved with the selection and use of the most influential parameters in machining decision-making. This work illustrates the development of two multicriteria indicators to optimize parameters for the abrasive waterjet machining process, providing optimization information for the surface morphology problem. The evaluation based on the distance from average solution (EDAS) method was used as the first indicator while the desirability function analysis (DFA) method reflects the second indicator. The results demonstrate a huge promise of both indicators, EDAS and DFA, to develop procedures for optimizing the parameters of Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy through the abrasive waterjet machining process. For the EDAS method, experimental trial 7 provided the best results with the water jet pressure of 220 bar, traverse speed of 40mm/min, and standoff distance of 1mm. The corresponding material removal rate is 151.667mm3/min while the roughness average is 2.76mm. The DFA method also provided the same results as those of the EDAS method. The present study is evidence of optimization of the parameters of Ti-6Al-2Sn-4Zr-2Mo alpha-beta alloy using the AWJM process. This warrants an intervention to enhance productivity and the economic gains of the company.