Improving the machinability and surface integrity of Inconel 718 is crucial to reducing the risk of premature failure and tool wear. Consequently, enhanced tool life and higher productivity can be achieved. This study aims to investigate the effects of cutting parameters (r, Vc, f, and ap) on surface roughness (Ra), tangential force (Fz), cutting power (Pc), and specific cutting pressure (Kc). Predictive models were created to assess the progression of Ra, Fz, Pc, and Kc, yielding R2 values of 95.79%, 95.57%, 97.20%, and 86.08%, respectively. The findings highlight that feed rate and depth of cut significantly influence Ra and Fz respectively. The input parameters were optimized using a mono objective optimization (Taguchi methodology based on signal-to-noise ratio). A comparative study between four MCDM methods (EDAS, PIV, MOORA, and ARAS) coupled with the Taguchi approach and the desirability function (DF) were used. This research has shown that the methods used yield highly satisfactory optimal results, which can be of interest to mechanical manufacturing companies and academic researchers in the field of machining Inconel 718, as well as in the field of optimization. To further explain the responses of each input parameter, the study examines the mechanisms of chip formation, shear instability, and segmented chip formation during the machining of Inconel 718.
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