Johnson–Cook Constitutive Parameters Identification Based on Multiobjective Optimization Strategy for Cutting Process Simulation

Accurate constitutive models are pivotal for reliable finite element simulations in machining optimization. Conventional methods for determining Johnson–Cook (JC) model parameters often lead to significant deviations in predicting material behavior under complex cutting conditions. This study introduces a hybrid multiobjective optimization strategy combining dynamic weighting with particle swarm optimization (PSO) and sequential least squares programming (SLSQP) to enhance the accuracy of JC parameter identification. The objective functions integrate mean absolute percentage error, residual sum of squares, and the coefficient of determination ( R 2 ) for a comprehensive evaluation. Comparative analyses demonstrate the superiority of the proposed method over NSGA‐II and MOEA/D algorithms. Validations through split Hopkinson pressure bar tests and turning simulations confirm its effectiveness, achieving cutting force predictions with an absolute error within 5.5 N and temperature prediction errors between 9.5% and 16%. These results represent a 15%–20% point improvement over traditional methods. The hybrid weighted‐PSO‐SLSQP strategy establishes a robust foundation for dependable material constitutive models in cutting simulations.