Determination of the Constants of GTN Damage Model Using Experiment, Polynomial Regression and Kriging Methods

Abstract

Damage models, particularly the Gurson-Tvergaard-Needleman (GTN) model, are widely used in numerical simulation of material deformations. Each damage model has some constants which must be identified for each material. The direct identification methods are costly and time consuming. In the current work, a combination of experimental, numerical simulation and optimization were used to determine the constants. Quasi-static and dynamic tests were carried out on notched specimens. The experimental profiles of the specimens were used to determine the constants. The constants of GTN damage model were identified through the proposed method and using the results of quasi-static tests. Numerical simulation of the dynamic test was performed utilizing the constants obtained from quasi-static experiments. The results showed a high precision in predicting the specimen's profile in the dynamic testing. The sensitivity analysis was performed on the constants of GTN model to validate the proposed method. Finally, the experiments were simulated using the Johnson-Cook (J-C) damage model and the results were compared to those obtained from GTN damage model.

A Review of Damage, Void Evolution, and Fatigue Life Prediction Models

“…For shear loading, the GTN model significantly overestimates the carrying capacity of materials [7] [11] [96]. Although it does not apparently consider the effect of strain rate, the model calculates the strain rate and consider its effect on the void growth depending on the loading rate and the predefined solution time [89]. Moreover, the model is suitable for the relatively low strain rates, while at higher strain rates, such as impact problems, the numerical simulation results are not accurate.…”

“…Examples include GTN model [85], Rice-Tracey model [86], Gunawardena model [87], the well-known strain rate dependent Johnson-Cook (JC) damage model [88], some other micro-mechanism based damage models and models based on continuum damage mechanics (CDM) theory. These empirical models are popular for engineering applications due to their simplicity, but the identification of parameters is costly, time consuming and lacks any scientific basis because they lack physical and mathematical foundations [89].…”

A machine learning based sensitivity analysis of the GTN damage parameters for dynamic fracture propagation in X70 pipeline steel

“…In the literature, a wide variety of calibration experiments and optimisation procedures can be found for the material-dependent parameters. Generally, a series of experiments is conducted and numerical models are created to fit the parameters as good as possible to the experimental data [44,45,46,47,48,49]. Otherless conventionalmethods implement complex subroutines or artificial neural networks [26,50].…”

“…A different selection of algorithm and/or their internal parameters such as stopping tolerance and objective function can result in different calibration results. For example, the study by Rahimidehgolan et al [44] showed that for different objective functions, different parameter values were obtained. However, extensive comparative studies on the influence of different optimisation algorithms and their respective parameters on the identified values are scarce.…”

FCG Modelling Considering the Combined Effects of Cyclic Plastic Deformation and Growth of Micro-Voids

“…Additionally, it is an internal and cumulative entity that cannot be measured directly [ 17 ]. Thus, this damage accumulation mechanism is usually modelled with the so called damage models, GTN (Gurson–Needleman–Tvergaard) being one of the most famous [ 18 ]. Note that the damage accumulation is not accounted for only by failure criteria [ 19 ] but also for the decrease in material stiffness, strength and a reduction in the remaining ductility [ 20 ].…”