A modified Johnson–Cook model for 7N01 aluminum alloy under dynamic condition

Zhang, Yi-ben; Song, Yong; Xin, Hu; Wang, Zhonggang

doi:10.1007/s11771-017-3668-5

Cited by 30 publications

(7 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The simulation of the MAF process was carried using ABAQUS/CAE 6.14-5 software. In this model, the Johnson-Cook relation shown below [31,32] was employed to describe the plastic behavior of the material:…”

Section: Finite Element Modelingmentioning

confidence: 99%

“…The properties and parameters of annealed at 750 • C nickel 200 alloy are summarized in Table 3. The constitutive model given by the Johnson-Cook equation can handle material under high strain rates, wide ranges of temperatures, and plastic deformation [31][32][33]. Other elastic and thermal properties are represented in Table 4.…”

Section: Finite Element Modelingmentioning

confidence: 99%

See 1 more Smart Citation

Strain Behavior of Nickel Alloy 200 during Multiaxial Forging through Finite Element Modeling

Djavanroodi

Hussain

Irfan

et al. 2019

Metals

View full text Add to dashboard Cite

Multiaxial forging (MAF) is one of the appealing methods of severe plastic deformation (SPD) techniques to fabricate ultrafine-grained (UFG) materials. In this study; the influence of process parameters such as strain rate; friction; and initial temperature has been assessed through finite element simulation of Nickel 200 alloy. The Johnson-Cook equation was applied in simulating the MAF process. The homogeneous microstructure of a material processed by MAF is an important requirement to obtain uniform mechanical and other properties. The uniformity in properties was evaluated by the investigation of the hardness measurements; effective strain (ES), and inhomogeneous factor (IF) or coefficient of standard deviation. The results showed that the inhomogeneous factor decreases with an increase in strain rate and decrease in temperature. It was found that a more homogeneous structure is observed with an increasing number of MAF cycles and the strain rate strain. Furthermore; the average grain size reduced from 850 nm to 220 nm after three cycles of MAF. Finally; experimental work was performed to validate the results.

show abstract

Section: Finite Element Modelingmentioning

confidence: 99%

Section: Finite Element Modelingmentioning

confidence: 99%

Strain Behavior of Nickel Alloy 200 during Multiaxial Forging through Finite Element Modeling

Djavanroodi

Hussain

Irfan

et al. 2019

Metals

View full text Add to dashboard Cite

show abstract

“…Many constitutive models have been proposed to describe the flow stress of material and they can mainly be divided into two types, i.e., phenomenological constitutive models and physics-based constitutive models. Phenomenological constitutive models include the Johnson Cook model 1 , 2 , Hockett-Sherby model 3 and Arrhenius model 4 , etc. For physical-based constitutive modelling, there are Zerilli–Armstrong model 5 and Rusinek–Klepaczko model 6 etc.…”

Section: Introductionmentioning

confidence: 99%

Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis

Chen

Shen

Zhang

2022

Sci Rep

View full text Add to dashboard Cite

Describing the material flow stress and the associated uncertainty is essential for the plastic stochastic structural analysis. In this context, a data-driven approach-heteroscedastic sparse Gaussian process regression (HSGPR) with enhanced efficiency is introduced to model the material flow stress. Different from other machine learning approaches, e.g. artificial neural network (ANN), which only estimate the deterministic flow stress, the HSGPR model can capture the flow stress and its uncertainty simultaneously from the dataset. For validating the proposed model, the experimental data of the Al 6061 alloy is used here. Without setting a priori assumption on the mathematical expression, the proposed HSGPR-based flow stress model can produce a better prediction of the experimental stress data than the ANN model, the conventional GPR model, and Johnson Cook model at elevated temperatures. After the HSGPR-based flow stress model is implemented into finite element analysis, two numerical examples with synthetic material properties are performed to demonstrate the model’s capability in stochastic plastic structural analysis. The results have shown that with sufficient data, the distribution of the structural load carrying capacity at elevated temperatures and the variation of load–displacement curves during the loading and unloading processes can be accurately predicted by the HSGPR-based flow stress model.

show abstract

“…To overcome this shortcoming, scholars established a modified Johnson–Cook model coupled with strain rate and deformation temperature [ 15 ]. With the modified Johnson–Cook model, the flow stress of aluminum alloy [ 16 ], magnesium alloy [ 17 ], and high strength steel [ 18 ] is accurately predicted under the condition of high temperature deformation. In addition, the application of an artificial neural network model based on experimental data and phenomena in the prediction of material flow stress is also gradually expanding [ 19 ].…”

Section: Introductionmentioning

confidence: 99%

Deformation Behavior and Constitutive Model of 34CrNi3Mo during Thermo-Mechanical Deformation Process

Jia

Zhou

2022

Materials

View full text Add to dashboard Cite

With higher creep strength and heat resistance, 34CrNi3Mo has been widely used in the production of engine rotors, steam turbine impellers, and turbine blades. To investigate the hot deformation behaviors of 34CrNi3Mo steel, hot compressive tests were conducted on a Gleeble-3500 thermomechanical simulator, under the temperature range of 1073 K–1373 K and strain rate ranges of 0.1 s−1–20 s−1. The results show that the flow stress of 34CrNi3Mo steel under high temperatures is greatly influenced by the deformation temperature and strain rate, and it is the result of the interaction between strain hardening, dynamic recovery, and recrystallization. Under the same deformation rate, as the deformation temperature increases, the softening effect of dynamic recrystallization and dynamic recovery gradually increases, and the flow stress gradually decreases. Under the same deformation temperature, with the increase of strain rate, the influence of strain hardening on 34CrNi3Mo steel is gradually in power, and the flow stress gradually increases. To predict the flow stress of 34CrNi3Mo steel accurately, a modified Arrhenius-type constitutive model considering the effects of strain, temperature, and strain rate at the same time was made based on the experiment data. On this basis, the evolution law of deformation activation and instability characteristics of 34CrNi3Mo steel were investigated, and the processing map of 34CrNi3Mo steel was established. The formability of 34CrNi3Mo steel under high temperature deformation was revealed, which provided a theoretical foundation of the equation of reasonable hot working process.

show abstract

A modified Johnson–Cook model for 7N01 aluminum alloy under dynamic condition

Cited by 30 publications

References 24 publications

Strain Behavior of Nickel Alloy 200 during Multiaxial Forging through Finite Element Modeling

Strain Behavior of Nickel Alloy 200 during Multiaxial Forging through Finite Element Modeling

Heteroscedastic sparse Gaussian process regression-based stochastic material model for plastic structural analysis

Deformation Behavior and Constitutive Model of 34CrNi3Mo during Thermo-Mechanical Deformation Process

Contact Info

Product

Resources

About