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Abstract: Accurate prediction of ductile fracture requires determining the material properties, including the parameters of the constitutive and ductile fracture model, which represent the true material response. Conventional calibration of material parameters often relies on a trial-and-error approach, in which the parameters are manually adjusted until the corresponding finite element model results in a response matching the experimental global response. The parameter estimates are often subjective. To address this issue, in this paper we treat the identification of material parameters as an optimization problem and introduce the particle swarm optimization (PSO) algorithm as the optimization approach. We provide material parameters of two uncoupled ductile fracture models—the Rice and Tracey void growth model (RT-VGM) and the micro-mechanical void growth model (MM-VGM), and a coupled model—the gurson-Tvergaard-Needleman (GTN) model for ASTM A36, A572 Gr. 50, and A992 structural steels using an automated PSO method. By minimizing the difference between the experimental results and finite element simulations of the load-displacement curves for a set of tests of circumferentially notched tensile (CNT) bars, the calibration procedure automatically determines the parameters of the strain hardening law as well as the uncoupled models and the coupled GTN constitutive model. Validation studies show accurate prediction of the load-displacement response and ductile fracture initiation in V-notch specimens, and confirm the PSO algorithm as an effective and robust algorithm for seeking ductile fracture model parameters. PSO has excellent potential for identifying other fracture models (e.‍g., shear modified GTN) with many parameters that can give rise to more accurate predictions of ductile fracture. Limitations of the PSO algorithm and the current calibrated ductile fracture models are also discussed in this paper.

基于粒子群算法的ASTM结构钢延性断裂模型参数识别研究

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