Revisiting Parametric Identification in Johnson–Cook Constitutive Equation Based on Multi-objective Optimization and 3D Metal Cutting Process Simulation

The Johnson–Cook (J–C) constitutive model is extensively used in numerical simulations. Traditional parameter-fitting methods often prioritize a single objective, which can compromise parameter applicability across diverse working conditions. Multi-objective fitting methods account for multiple mechanical behaviors concurrently, thereby improving both model accuracy and generalizability. In this study, mechanical tests covering strain rates from 0.001 to 1900/s and temperatures ranging from 20 to 310 °C were performed, along with microstructural observations under varying strain rates. Three parameter sets, derived using traditional methods and the multi-objective optimization approach, were applied in finite element tensile simulations. Model suitability was assessed through comparisons of the correlation coefficient (R), average absolute relative error, and prediction relative error. Their accuracy was further validated through cutting simulation. The results demonstrated that the multi-objective optimization method substantially reduced discrepancies between simulated and experimental cutting forces, achieving prediction errors below 20%.