Constitutive analysis and processing map of an extruded Mg–3Gd–1Zn alloy under hot shear deformation

Sarebanzadeh, M.; Mahmudi, R.; Roumina, R.

doi:10.1016/j.msea.2015.04.038

Cited by 61 publications

(21 citation statements)

References 27 publications

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“…As indicated, the Q values of the present TAZ811 were relatively high compared to that of conventional Mg-AlZn [22] and Mg-Zn-Al based [39] alloys, but lower than that in the Mg-Gd-Zn [40] and Mg-Gd-Y-Zn alloys [29]. It was indicated that the higher Q values as to the self-diffusion of Mg (135 kJ/mol) were mainly due to the back stress caused by the presence of intermetallic particles in the matrix [15,29].…”

Section: Comparison With Deformation Behaviors Other Mg Alloysmentioning

confidence: 59%

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Optimum parameters and kinetic analysis for hot working of a homogenized Mg–8Sn–1Al–1Zn alloy

Cheng

You

et al. 2015

Materials & Design

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Section: Comparison With Deformation Behaviors Other Mg Alloysmentioning

confidence: 59%

“…The practical temperature of the present homogenized TAZ811 alloy was much lower than those of homogenized Mg-Gd-Zn [40] and Mg-Gd-Y-Zn [29] alloys. Meanwhile, the practical strain rate was much higher than the Mg-Al-Zn [22], Mg-Zn-Al [39], Mg-Gd-Zn [40] and Mg-Gd-Y-Zn [29] alloys.…”

Section: Comparison With Deformation Behaviors Other Mg Alloysmentioning

confidence: 62%

Optimum parameters and kinetic analysis for hot working of a homogenized Mg–8Sn–1Al–1Zn alloy

Cheng

You

et al. 2015

Materials & Design

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“…Recently, shear punch testing (SPT) needing very small thin samples was successfully used for evaluating the hot deformation characteristics of Mg-Li alloys [21,22] and Mg-Gd-Zn alloys [23] through constitutive analysis. Thus, the present investigation was initiated to investigate the microstructural and textural evolution of an Mg-5Gd-4Y-0.4Zr alloy during hot shear deformation using SPT at different temperatures and strain rates and to determine the underlying restoration processes and also the role of the RE-rich precipitates in enhancing the high-temperature behavior of this alloy.…”

Section: Introductionmentioning

confidence: 99%

Microstructural evolution during hot shear deformation of an extruded fine-grained Mg–Gd–Y–Zr alloy

et al. 2017

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Abstract:Mg-Gd-Y-Zr alloys are among recently developed Mg alloys having superior mechanical properties at elevated temperatures. Dynamic recrystallization (DRX) and rare earth (RE)-rich particles play important roles in enhancing the high temperature strength of these alloys. Accordingly, the microstructural evolution of a fine grained extruded Mg-5Gd-4Y-0.4Zr alloy was investigated after hot shear deformation in the temperature range of 350-450 °C using the shear punch testing (SPT) method. The results reveal the occurrence of partial dynamic recrystallization at the grain boundaries at 350 °C while the fraction of DRX grains increases with increasing deformation temperature. A fully-recrystallized microstructure was achieved after SPT at 450 °C. The Gd-rich and Y-rich cuboid particles, having typical sizes in the range of ~50 nm to ~3 m, show excellent stability and compatibility after hot shear deformation and these particles enhance the high temperature strength during hot deformation at elevated temperatures. The textural evolution, examined using electron backscattered diffraction (EBSD), revealed a non-fibrous basal DRX texture after SPT which is different from the conventional deformation texture.

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“…Therefore, empirical models do not need details about physical or chemical revolution involved during the deformation process. The Arrhenius-type constitutive model is recognized to be one of the most commonly used empirical models and has been successfully applied to various alloys, including magnesium alloys [9], titanium alloys [10,11], aluminum alloys [12] and steels [13]. In contrast, the artificial neural network (ANN) model does not refer to any mathematical model, and it only learns from examples and recognizes patterns from a series of inputs and outputs without any prior assumptions about their nature.…”

Section: Introductionmentioning

confidence: 99%

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

Han

Yan

Sun

et al. 2017

Metals

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Abstract:In this work, the hot deformation behavior of 6A02 aluminum alloy was investigated by isothermal compression tests conducted in the temperature range of 683-783 K and strain-rate range of 0.001-1 s −1 . According to the obtained true stress-true strain curves, the constitutive relationship of the alloy was revealed by establishing the Arrhenius-type constitutive model and back-propagation (BP) neural network model. It is found that the flow characteristic of 6A02 aluminum alloy is closely related to deformation temperature and strain rate, and the true stress decreases with increasing temperatures and decreasing strain rates. The hot deformation activation energy is calculated to be 168.916 kJ mol −1 . The BP neural network model with one hidden layer and 20 neurons in the hidden layer is developed. The accuracy in prediction of the Arrhenius-type constitutive model and BP neural network model is eveluated by using statistics analysis method. It is demonstrated that the BP neural network model has better performance in predicting the flow stress.

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Constitutive analysis and processing map of an extruded Mg–3Gd–1Zn alloy under hot shear deformation

Cited by 61 publications

References 27 publications

Optimum parameters and kinetic analysis for hot working of a homogenized Mg–8Sn–1Al–1Zn alloy

Optimum parameters and kinetic analysis for hot working of a homogenized Mg–8Sn–1Al–1Zn alloy

Microstructural evolution during hot shear deformation of an extruded fine-grained Mg–Gd–Y–Zr alloy

Modeling the Constitutive Relationship of Al–0.62Mg–0.73Si Alloy Based on Artificial Neural Network

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