Estimating Johnson-Cook Material Parameters using Neural Networks

The five-parameter Johnson-Cook (J-C) material model represents the behavior of a material under extreme mechanical loading, including high temperatures, strains, and strain rates. The goal of this study is to estimate five J-C material parameters and chip thickness jointly for a given set of force components, power, and temperature. The approach uses two neural network models on a dataset simulated by finite element analysis for orthogonal cutting of aluminum 6061-T6. The first model develops a function approximator to predict the force components, power, and temperature using a given set of J-C parameters and chip thickness for aluminum 6061-T6. The second model searches the input space of the first model to estimate the J-C parameter values and chip thickness, given a set of targeted force components, power, and temperature of interest. The performance of both neural network models is evaluated using mean absolute percentage error. The results suggest that the developed neural networks-based approach is capable of estimating multiple J-C parameters and chip thickness that will result in a targeted force components, power, and temperatures of interest, given starting ‘educated guesses’ about these values.