Inverse identification of material constitutive parameters based on co-simulation

Abstract

Establishing a material constitutive model reflecting the mechanical behavior of metal materials in the cutting process is the basis of cutting finite element simulation, and has an important guiding role in accurately describing the cutting thermo-mechanical coupling state variables and further optimizing process parameters. This paper proposes a reverse identification method of material constitutive parameters based on co-simulation. The influence of mass scaling factor, mesh size, and Johnson-Cook (J-C) constitutive parameters on cutting force was analyzed, and orthogonal cutting experiments were carried out for two difficult-to-machine materials, die steel H13 and titanium alloy Ti-6Al-4V. ABAQUS is redeveloped based on Python, and then the J-C constitutive parameters are directly optimized for the first time with the minimum error between the finite element simulation value and the experimental value of the cutting force as the objective function, the least square identification of J-C constitutive parameters based on co-simulation is realized by combining genetic algorithm. The results show that the reasonable setting of mass scaling factor and mesh size can significantly reduce the time of simulation calculation and improve the optimization efficiency of constitutive parameters. The yield stress, hardening coefficient, strain rate coefficient, hardening coefficient and softening coefficient of materials have different effects on cutting force, which is the key to effective inverse identification of constitutive parameters. Compared with different J-C parameters reported in the literature, it is found that the constitutive parameters obtained by inverse identification based on co-simulation are more accurate, and the prediction of residual stress and serrated chip characteristics also has good accuracy. This study provides a reference for the optimization of constitutive parameters, and the research results provide a new scheme for the efficient construction of material mechanical behavior required for cutting finite element simulation.

Keywords

• Finite element
• Co-simulation
• Genetic algorithm
• J-C constitutive model
• Inverse identification