Investigations on the Choice of Johnson–Cook Constitutive Model Parameters for the Orthogonal Cutting Simulation of Inconel 718

The Johnson–Cook model is the popular constitutive relation for the simulation of metal cutting process because of the availability of various sets of parameters for different materials. Different sets of Johnson–Cook parameters are observed to be available for a particular material due to dissimilarity in the initial condition of the material and experimental methods utilized for the calibration. Hence, it is difficult to choose an accurate parameter set for modeling the behavior of a material for the finite element simulation of its cutting process. In this regard, a strategy is proposed and validated in this study for choosing an accurate Johnson–Cook parameter set for Inconel 718. Twelve sets of Johnson–Cook parameters were collected from the literature for Inconel 718 and their flow stress predictions were investigated by comparing with experimental flow stress values for wide ranges of strain rates and temperatures as encountered during the cutting process. The Johnson–Cook parameter sets corresponding to the predicted flow stress curves that agree with the experimental values are chosen for modeling the behavior of Inconel 718. This comparison also helps in indexing the parameter sets according to the initial condition of the material since it is not reported mostly in the literature. The chosen material parameter sets are validated further for the orthogonal cutting simulation of Inconel 718 for a wide range of cutting conditions by comparing the cutting force and chip thickness predictions with the experimental values. Thus, the analysis of predicted flow stress values helps in choosing the accurate Johnson–Cook parameters for an Inconel 718 specimen which in turn helps in conducting accurate orthogonal cutting simulations. The finite element simulation of metal cutting helps in identifying the optimum cutting parameters which would help to reduce energy consumption and thus make the process more efficient and sustainable.