Quasi-Static Loading Responses and Constitutive Modeling of Al–Si–Mg alloy

Liang, Zhenglong; Zhang, Qi

doi:10.3390/met8100838

Cited by 13 publications

(9 citation statements)

References 35 publications

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“…Its simplicity, straightforward formulation, and ease of estimating material constants have made it used by researchers to forecast material flow behavior [30]. The JC model can be represented as [30][31][32][33]:…”

Section: Johnson-cook Modelmentioning

confidence: 99%

Warm Deformation Behavior and Flow Stress Modeling of AZ31B Magnesium Alloy under Tensile Deformation

Murugesan

Chung

et al. 2023

Materials

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Constitutive equations were recognized for AZ31B magnesium alloy at higher temperatures and strain rates from conventional empirical models like the original Johnson–Cook (JC), modified JC, and modified Zerilli–Armstrong (ZA) models for capturing the material warm deformation behavior. Uniaxial warm tensile tests were performed at temperatures (50 to 250 °C) and strain rates (0.005 to 0.0167 s−1) to probe AZ31 magnesium alloy flow stress values. Depending on the calculated flow stress, constitutive equations were recognized, and these established models were assessed by the coefficient of determination (R2), relative mean square error (RMSE), and average absolute relative error (AARE) metrics. The results demonstrated that the flow stress calculated by the modified JC and ZA models revealed good agreement against the test data. Thus, the outcomes confirmed that the recognized modified JC and modified ZA models could effectively forecast AZ31 magnesium alloy flow behavior by capturing the material deformation behavior accurately.

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Section: Johnson-cook Modelmentioning

confidence: 99%

Warm Deformation Behavior and Flow Stress Modeling of AZ31B Magnesium Alloy under Tensile Deformation

Murugesan

Chung

et al. 2023

Materials

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“…Studies showed that Johnson–Cook (JC) and modified Johnson–Cook (MJC) models are widely used to predict material behavior under hot deformation conditions. Therefore, Song et al, [ 9 ] Wang et al, [ 10 ] Tan et al, [ 11 ] Liang et al, [ 12 ] and Li et al, [ 13 ] had investigated the selected material dynamical behavior at higher strain rates and elevated temperatures using JC and MJC models. They documented that the MJC model could accurately describe the studied material's flow behavior.…”

Section: Introductionmentioning

confidence: 99%

“…They documented that the MJC model could accurately describe the studied material's flow behavior. For example, Liang et al [ 12 ] examined Al–Si–Mg alloy material at strain rates of 10 −3 s −1 and deformation temperatures of to 500 °C. They reported that the prediction error from the MJC model was about 1.65% and held well‐agreement with the actual data.…”

Section: Introductionmentioning

confidence: 99%

Supervised Machine Learning Approach for Modeling Hot Deformation Behavior of Medium Carbon Steel

Murugesan

Jung

et al. 2022

steel research int.

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The hot metalworking process is a typical procedure in the metallurgy industries for manufacturing many daily-life products that can not be produced using the cold working process. [1,2] The crucial advantages of the hot forming process are the higher material formability. Besides, the produced parts have no oriented grain structure due to hot processing conditions, resulting in highly isotropic strength characteristics. [3,4] In the metal forming process, the material will experience various loading conditions through strain, stress, strain rate, and temperature. Therefore, the material can be tested at different temperatures to explain the deformation behavior and strain rates, and besides essential key parameters can also be obtained. [5,6] The constitutive model can be established and implemented into the finite element analysis (FEA) software using received stressstrain (SS) curves for performing forming simulations to save experimental time and cost. [7] Many researchers have been devising various constitutive models by establishing the relationship between the external loading conditions and the thermomechanical behavior of test materials. [8] Because precise flow stress models could be exploited to perform accurate forming simulations, and optimal forming conditions can eventually be received. Studies showed that Johnson-Cook (JC) and modified Johnson-Cook (MJC) models are widely used to predict material behavior under hot deformation conditions. Therefore, Song et al., [9] Wang et al., [10] Tan et al., [11] Liang et al., [12] and Li et al., [13] had investigated the selected material dynamical behavior at higher strain rates and elevated temperatures using JC and MJC models. They documented that the MJC model could accurately describe the studied material's flow behavior. For example, Liang et al. [12] examined Al-Si-Mg alloy material at strain rates of 10 À3 s À1 and deformation temperatures of to 500 °C. They reported that the prediction error from the MJC model was about 1.65% and held well-agreement with the actual data. Similarly, Li et al. [13] studied T24 steel material at hot deformations using the MJC model. They also confirmed that the proposed model wellcaptured the material's dynamic behavior and agreed with the experimental observations.Researchers have also adopted the strain compensated constitutive equation, repeatedly called, the Arrhenius-Type constitutive model, to describe the material hot deformation characteristics. For example, researchers Zhao et al., [14] Li et al., [15] Zhang et al., [16] Liu et al., [17] and Li et al. [18] had exploited the Zener-Hollomon exponent-type equation for the hard-toform materials such as 14Cr ODS steel, 2219 aluminum, TA15 titanium, titanium-aluminum, and magnesium alloys, respectively. Liu et al.

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“…However, during the metal forming process, metals and alloys undergo an inhomogeneous deformation by cause of hot operating conditions. For this reason, understanding the metals deformation behavior is necessary for determining the working parameters that affect the mechanical properties for providing the well-defined material processing data to the industry [1][2][3][4]. The constitutive equations are often utilized in a form that is suitable to use in finite element (FE) commercial tools.…”

Section: Introductionmentioning

confidence: 99%

Microstructure Evaluation and Constitutive Modeling of AISI-1045 Steel for Flow Stress Prediction under Hot Working Conditions

2020

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In the field of engineering, automobile and aerospace components are manufactured based on the desired applications from the metal forming process. For producing better quality of both symmetry and asymmetry mechanical parts, understanding the material deformation and analytical representation of the material ductility behavior for the working material is necessary as the forming procedures carried out mostly in the warm processing conditions. In this work, the hot tensile test flow stress-strain data were utilized to construct the constitutive equation for describing AISI-1045 steel material hot deformation behavior, and the test conditions, such as deformation temperatures and strain rates were 750–950 ° C and 0.05–1.0 s − 1 , respectively. The surface morphology and elemental identification analysis were performed using the field emission scanning electron microscopy (FESEM) coupled with the energy-dispersive X-ray spectroscopy (EDS) mapping setup. In this work, the Arrhenius-type constitutive equation, including the strain compensation, was used to formulate the flow stress prediction model for capturing the material behavior. Besides, the Zener-Hollomon parameter was altered, employing incorporating the effect of strain rate and strain on the flow stress. The empirical model approach was employed to estimate the material model constants from the constitutive equation using the actual test measurements. The population metrics such as coefficient of determination ( R 2 ), sample standard deviation of the error (SSD), standard error of the regression (SER), coefficient of residual variation (CRV), and average absolute relative error (AARE) was employed to confirm the predictability of the proposed models. The computed results are discussed in detail, using numerical and graphical verification’s. From the graphical comparison, the flow stress-strain data achieved from the proposed constitutive model are in good agreement with the actual test measurements. The constitutive model prediction accuracy is found to be improved, like the prediction error range from 3.678% to 2.984%. This evidence proves to be feasible as the newly developed model displayed a significant improvement against the experimental observations.

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Quasi-Static Loading Responses and Constitutive Modeling of Al–Si–Mg alloy

Cited by 13 publications

References 35 publications

Warm Deformation Behavior and Flow Stress Modeling of AZ31B Magnesium Alloy under Tensile Deformation

Warm Deformation Behavior and Flow Stress Modeling of AZ31B Magnesium Alloy under Tensile Deformation

Supervised Machine Learning Approach for Modeling Hot Deformation Behavior of Medium Carbon Steel

Microstructure Evaluation and Constitutive Modeling of AISI-1045 Steel for Flow Stress Prediction under Hot Working Conditions

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