Research on inverse identification of johnson-cook constitutive parameters for turning 304 stainless steel based on coupling simulation

The constitutive model plays a decisive role in the accuracy and reliability of the simulation results. Because the material flow characteristics obtained by the traditional experimental method are difficult to describe thermoplastic deformation behavior in the cutting process accurately, and the inverse identification is usually carried out based on a two-dimensional orthogonal cutting model, ignoring the actual cutting state, which leads to significant deviations in the results of parameter identification. Therefore, a three-dimensional (3-D) turning finite element simplified model which conforms to the actual cutting scenario was established. Based on this model, a coupling simulation was used to inverse identify the Johnson-Cook (J-C) parameters for turning 304 stainless steel. Firstly, the automatic finite element modeling and calculation of 3-D turning is implemented by Python. Then, the inverse identification framework was built based on the ISIGHT. The initial yield strength of J-C parameters was obtained from quasi-static compression experiments. With the objective function of minimizing the error between the experimental cutting force and the simulated cutting force, the inverse identification of J-C parameters (strain reinforcement factor, strain rate sensitivity, thermal softening index, and hardening index) was carried out by a multi-island genetic algorithm. Finally, compared with cutting forces, chip morphology, and residual stresses, the feasibility of the inverse identification method and the accuracy of the constitutive model is demonstrated, which also provides a reference for the optimal identification method of constitutive parameters of difficult-to-machine materials.