Determination of Johnson–Cook parameters and evaluation of Charpy impact test performance for X80 pipeline steel

Cao, Yanjie; Zhen, Ying; Song, Ming; Yi, Haijiao; Li, Fagen; Li, Xuyang

doi:10.1016/j.ijmecsci.2020.105627

Cited by 66 publications

(29 citation statements)

References 14 publications

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“…According to Bridgman's analysis method, assuming that the fracture occurs at the center of the incision, the parameters D 1 , D 2 , and D 3 can be obtained from the quasi-static tensile test of the smooth and axisymmetric specimens of the incision [16]. The stress triaxiality at the center of the specimen notch is expressed as where r f is the radius of the minimum cross section, and R f is notch radius at specimen fracture.…”

Section: Acquisition Of Parameters D 1 D 2 and Dmentioning

confidence: 99%

Construction of High Strain Rate Loading Constitutive Model and Failure Model and Prediction of Forming Limit for LA103Z Magnesium Alloy

Yang

Wang

et al. 2021

Met. Mater. Int.

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In order to use numerical simulation to reveal the formability of magnesium-lithium alloys and other light alloys under high strain rate, it is crucial to construct a constitutive model and a forming limit diagram (FLD) of LA103Z alloy that was formed by laser shock at high strain rate. In this study, three different uniaxial tensile experiments on the ultralight alloy material LA103Z magnesium alloy were conducted, the mechanical behavior of the material with sensitivity to high strain rate was studied, and a Johnson-Cook (JC) constitutive model sensitive to high strain rates and suitable for laser shock forming was proposed. At the same time, the constitutive parameters and failure parameters of the modified JC model of LA103Z magnesium alloy were obtained through three different uniaxial tensile experiments. On the basis of the relationship between tensile loading and stress-strain of LA103Z magnesium alloy, the experiments demonstrated the precision of the modified JC constitutive model. A modified constitutive model was then developed by using the user material subroutine VUMAT, and then it was applied to ABAQUS. Finally, the formability of the material was simulated, and the FLD of LA103Z magnesium alloy material in laser shock forming was predicted. This study provides guidance on the actual production and application of LA103Z magnesium alloys.

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Section: Acquisition Of Parameters D 1 D 2 and Dmentioning

confidence: 99%

Construction of High Strain Rate Loading Constitutive Model and Failure Model and Prediction of Forming Limit for LA103Z Magnesium Alloy

Yang

Wang

et al. 2021

Met. Mater. Int.

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show abstract

“…Therefore, when selecting materials for structural design, the impact resistance of materials is as important a performance index as the strength of materials [ 3 , 4 , 5 ]. However, a large number of studies have shown that the variation law of impact energy with specimen size is not linear, and anomalies occur at small sizes [ 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. Meanwhile, impact toughness decreases with the increase in specimen size [ 6 , 10 , 13 ].…”

Section: Introductionmentioning

confidence: 99%

Study on Intrinsic Influence Law of Specimen Size and Loading Speed on Charpy Impact Test

Wang

Zhang

et al. 2022

Materials

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Charpy impact energy/impact toughness is closely related to external factors such as specimen size. However, when the sample size is small, the linear conversion relationship between the Charpy impact energy of the sub-size and full-size Charpy specimens does not hold; the Charpy impact toughness varies with the size of the specimen and other factors. This indicates that studying the internal influence of external factors on impact energy or impact toughness is the key to accurately understanding and evaluating the toughness and brittleness of materials. In this paper, the effects of strain rate on the flow behavior and the effects of stress triaxiality on the fracture behavior of 30CrMnSiNi2A high-strength steel were investigated using quasi-static smooth bar and notched bar uniaxial tensile tests and Split Hopkinson Tensile Bar (SHTP). Based on the flow behavior and strain rate dependences of the yield behavior, a modified JC model was established to describe the flow behavior and strain rate behavior. Charpy impact tests were simulated using the modified JC model and JC failure model with the determined parameters. Reasonable agreements between the simulation and experimental results have been achieved, and the validity of the model was proved. According to the simulation results, the impact energy was divided into crack initiation energy, crack stability propagation energy and crack instability propagation energy. On this basis, the effects of striker velocity and specimen width on the energy and characteristic load of each part were studied. The results show that each part of the impact energy has a negligible dependence on the hammer velocity, but there is a significantly different positive linear relationship with the width of the sample. The energy increment of each part also showed an inverse correlation with the increase in the sample width. The findings reveal that the internal mechanism of Charpy impact toughness decreases with the increase in sample width; to a certain extent, it also reveals the internal reason why the linear transformation relationship of Charpy impact energy between sub-size specimens and standard specimens is not established when the specimens are small. The analytical method and results presented in this paper can provide a reference for the study of the dynamic behavior of high-strength steel, the relationship between material properties and sample size, and the elastic–plastic impact dynamic design.

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“…It is also of great research value to transform non-standard test results into standard results. Li [5]studied the influence of the radius of the striker on the energy absorbed by the impact of X80 steel for pipelines; Madhusudhan [6] presented the variation of energy absorbed by changing the pendulum speeds of 5 m/s, 6 m/s, 7 m/s and 9 m/s of maraging steel 300 using Abaqus software. For X70 pipeline steel, there is a lack of comprehensive and systematic analysis of factors influencing the results of Charpy impact testing; so the corresponding damage models can be validated and / or calibrated to be implemented in more complex simulations.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

Ouladbrahim

Belaidi

Khatir

et al. 2021

Frattura Ed Integrità Strutturale

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In this paper, the initial and maximum load was studied using the Finite Element Modeling (FEM) analysis during impact testing (CVN) of pipeline X70 steel. The Gurson-Tvergaard-Needleman (GTN) constitutive model has been used to simulate the growth of voids during deformation of pipeline steel at different temperatures. FEM simulations results used to study the sensitivity of the initial and maximum load with GTN parameters values proposed and the variation of temperatures. Finally, the applied artificial neural network (ANN) is used to predict the initial and maximum load for a given set of damage parameters X70 steel at different temperatures, based on the results obtained, the neural network is able to provide a satisfactory approximation of the load initiation and load maximum in impact testing of X70 Steel.

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Determination of Johnson–Cook parameters and evaluation of Charpy impact test performance for X80 pipeline steel

Cited by 66 publications

References 14 publications

Construction of High Strain Rate Loading Constitutive Model and Failure Model and Prediction of Forming Limit for LA103Z Magnesium Alloy

Construction of High Strain Rate Loading Constitutive Model and Failure Model and Prediction of Forming Limit for LA103Z Magnesium Alloy

Study on Intrinsic Influence Law of Specimen Size and Loading Speed on Charpy Impact Test

Sensitivity analysis of the GTN damage parameters at different temperature for dynamic fracture propagation in X70 pipeline steel using neural network

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