Study on Ductility Failure of Advanced High Strength Dual Phase Steel DP590 during Warm Forming Based on Extended GTN Model

Liu, Xingfeng; Li, Di; Song, Hui; Lu, Zipeng; Cui, Hongjian; Ning, Jing; Xu, Jiang

doi:10.3390/met12071125

Cited by 4 publications

(3 citation statements)

References 32 publications

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“…They allow for the determination of the rheological properties of the materials at any thermomechanical parameter, such as temperature, strain, and strain rate. Such models may also be useful for the simulation of the deformation by the finite element method [ 9 , 10 , 11 , 12 , 13 , 14 ]. Currently, a lot of constitutive models have been constructed for metallic materials [ 15 , 16 , 17 , 18 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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The development of new lightweight materials is required for the automotive industry to reduce the impact of carbon dioxide emissions on the environment. The lightweight, high-manganese steels are the prospective alloys for this purpose. Hot deformation is one of the stages of the production of steel. Hot deformation behavior is mainly determined by chemical composition and thermomechanical parameters. In the paper, an artificial neural network (ANN) model with high accuracy was constructed to describe the high Mn steel deformation behavior in dependence on the concentration of the alloying elements (C, Mn, Si, and Al), the deformation temperature, the strain rate, and the strain. The approval compression tests of the Fe–28Mn–8Al–1C were made at temperatures of 900–1150 °C and strain rates of 0.1–10 s−1 with an application of the Gleeble 3800 thermomechanical simulator. The ANN-based model showed high accuracy, and the low average relative error of calculation for both training (5.4%) and verification (7.5%) datasets supports the high accuracy of the built model. The hot deformation effective activation energy values for predicted (401 ± 5 kJ/mol) and experimental data (385 ± 22 kJ/mol) are in satisfactory accordance, which allows applying the model for the hot deformation analysis of the high-Mn steels with different concentrations of the main alloying elements.

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Section: Introductionmentioning

confidence: 99%

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Churyumov

Kazakova

2023

Materials

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“…The damage coupled model integrates the constitutive properties of materials, and it can predict the deterioration of mechanical properties caused by damage accumulation [29,30]. The Gurson-Tvergaard-Needleman (GTN) model is one of the well-known damage coupled models, and was developed on the basis of a series of researchers' achievements [36][37][38]. Firstly, Gurson proposed the initial porous plasticity model [39].…”

Section: Introductionmentioning

confidence: 99%

The Simulation of Extremely Low Cycle Fatigue Fracture Behavior for Pipeline Steel (X70) Based on Continuum Damage Model

Fang

Sun

et al. 2023

Metals

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Natural gas transmission pipelines installed in seismic and permafrost regions are vulnerable to cyclic loads with a large strain amplitude. Under these conditions, the pipe may fail in extremely low cycles, a situation which is also known as extremely low cycle fatigue (ELCF) failure. The fracture mechanism of ELCF shows significant difference to that of low cycle fatigue, and the ELCF life usually deviates from the Coffin–Manson law. Thus, it is essential to develop an effective model to predict ELCF failure of the pipeline. In this study, a series of ELCF tests is performed on pipeline steel (X70). A damage coupled mixed hardening model is developed to simulate the fracture behaviors. Continuum damage law under monotonic load is extended to cyclic load by introducing the effective equivalent plastic strain. By assuming the cyclic softening is induced by the damage accumulation, the damage parameters are fitted directly from the peak stress in each cycle. Then, the model is input into commercial software ABAQUS with a user material subroutine to simulate the fracture behaviors of these specimens. The simulation results show good agreements with the test results both under cyclic and monotonic load, which verifies the reliability of the model.

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“…Gurson-type models were applied to predict failure in a wide range of manufacturing processes such as hydroforming process, 12 incremental forming, 13 and spinning 14 as well as advanced manufacturing processes such as additive manufacturing. 15 In addition, the GTN model has great capability to predict the fracture behavior of different ductile materials, such as aluminum, 16 steel, 17 and even polymer. 18 However, the original GTN model has no ability to analyze behavior of damage under low-stress triaxiality.…”

Section: Introductionmentioning

confidence: 99%

Fracture prediction of AA6061-T6 sheet in bending process using Gurson–Tvergaard–Needleman model

Khademi

Naeini

Mirnia

et al. 2022

Proceedings of the Institution of Mechanical Engineers, Part L:

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Fracture is one of the limiting factors of sheet metal forming processes. For this reason, finite element simulations are widely used for providing suitable forming approaches. However, the success of these approaches depends on the correct modeling of fracture in the simulations. In this paper, the fracture behavior of the Aluminum 6061-T6 sheet in the U-bending process is investigated by applying the Gurson–Tvergaard–Needleman model. For this purpose, the Gurson–Tvergaard–Needleman model is defined in the ABAQUS software by means of a proper subroutine. The Gurson–Tvergaard–Needleman model is also modified to take into account the anisotropy effect on the yield surface and the damage evolution due to shear stresses. Then, the proposed models are calibrated using the inverse analysis method based on tension tests with different stress states. The deformation mechanics analysis reveals that considering the normalized Lode angle parameter in the damage function increases the accuracy of fracture prediction, especially in low-stress triaxiality. Comparing the predicted fracture displacements with the experimental value in the U-bending process shows that the modified Gurson–Tvergaard–Needleman model calibrated by the uniaxial tension and in-plane shear tension tests can predict the onset of fracture with acceptable accuracy in the bending process while the original Gurson–Tvergaard–Needleman should be calibrated by a test with stress state close to that in the bending area to have such a result.

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Study on Ductility Failure of Advanced High Strength Dual Phase Steel DP590 during Warm Forming Based on Extended GTN Model

Cited by 4 publications

References 32 publications

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

Prediction of True Stress at Hot Deformation of High Manganese Steel by Artificial Neural Network Modeling

The Simulation of Extremely Low Cycle Fatigue Fracture Behavior for Pipeline Steel (X70) Based on Continuum Damage Model

Fracture prediction of AA6061-T6 sheet in bending process using Gurson–Tvergaard–Needleman model

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