Tool vibration, surface roughness, cutting power, and productivity assessment using RSM and GRA approach during machining of Inconel 718 with PVD-coated carbide tool

Nickel-based alloys such as (Inconel 718) are widely used in the fields of space technology, rocket engines, nuclear reactors, petrochemicals, submarines, power plants, and others. Due to its high mechanical properties, low thermal conductivity, and other characteristics, it is classified as a difficult material to machine. The main objective of this work is to model the tool vibration (Vtng), surface roughness (Ra), cutting force (Fz), power consumption (Pc), and material removal rate (MRR) as a function of different cutting conditions such as cutting speed (Vc), feed rate (f), depth of cut (ap), and tool nose radius (r). Machining tests were performed by dry turning of Inconel 718 with a PVD-coated metal carbide tool, following a Taguchi L27 (3^4) design of experiment. The response surface methodology (RSM) and the analysis of variance (ANOVA) were used to develop prediction models. The desirability function (DF) and the gray relational analysis (GRA) method were used and compared to determine the optimal cutting conditions, in order to minimize the outputs (Vtng, Ra, Fz, and Pc), and maximize the (MRR), according to two study cases encountered in industry.