Multiple solutions of tungsten carbide-cobalt cutting tool turning performance optimization results by Taguchi's combined multiobjective approach analysis an exponentiated exponential Weibull Dagum distribution model

Manufacturers employ various techniques to enhance tool life and influence both production costs and surface finish quality. Recently, cryogenic treatment has gained attention for improving machining performance. In this study, tungsten carbide-cobalt (WC–Co) cutting tools underwent cryogenic treatment at −196 °C for 24 h followed by tempering at 200 °C for 2 h. Machinability characteristics including Tool Wear Rate and Surface Roughness (Ra) were assed for Cryo Treated (CT) cutting tool during spinning operations on Al6063 alloy. This evaluation was compared with an untreated tool. Results demonstrate that the CT tool significantly enhances tool longevity while ensuring a satisfactory surface finish. To optimize the performance of the CT tool in turning processes, multiobjective optimization techniques, namely Taguchi's Grey Relational Analysis and Technique for Order Preference by Similarity to Ideal Solution, were utilized. In this research exponentiated exponential Weibull Dagum (EEWD) model has been utilized with the goal of increasing the tractability of the DGM (Dagum) in modeling the real-world data. The main object of this work, an innovative generalization of the EEWD distribution of Quantile Function (QntFEEWD) has been derived with six parameters. This QntFEEWD is significantly influent in driving measures of position such as the Qutl (quartiles), Octl (octiles), Decl (deciles), Pecl (percentiles), and median of EEWD distribution. The T-R{.} framework has been recently used to generalize different distributions; however, the DGM distribution's viability has not been examined. The T-R{.} combines three distributions, with one acting as a standard distribution. Each distribution's combined potency, which is a weighted hazard function of the baseline distribution, would have more parameters but be able to handle dataset bimodality with great flexibility. In order to generalize the DGM distribution, this paper used the quantile function of the Weibull distribution. The current study presents the EEWD, a novel generalized six parameter (6P) model.