Hot deformation behavior and 3D processing maps of AA7020 aluminum alloy

Ke, Bin; Ye, Lingying; Tang, Jianguo; Zhang, Yong; Liu, Shengdan; Lin, Huaqiang; Dong, Yu; Liu, Xiaodong

doi:10.1016/j.jallcom.2020.156113

Cited by 87 publications

(25 citation statements)

References 52 publications

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“…In order to design an optimal plastic forming process for Ti6Al4V alloy, it was necessary to research forming conditions of its hot working. A processing map based on the dynamic material model (DMM) was superimposed on a power dissipation diagram and flow instability diagram, which made it easy to observe the distribution of the power dissipation coefficient under different deformation conditions and avoid flow instability caused by adiabatic shear bands [ 44 ].…”

Section: Resultsmentioning

confidence: 99%

Constitutive Model Parameter Identification Based on Optimization Method and Formability Analysis for Ti6Al4V Alloy

Chen

Zhang

et al. 2022

Materials

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Titanium alloy is widely applied in aerospace, medical, shipping and other fields due to its high specific strength and low density. The purpose of this study was to analyze the formability of Ti6Al4V alloys at elevated temperatures. An accurate constitutive model is the basic condition for accurately simulating the plastic forming of materials, and it is an important basis for optimizing the parameters of the hot forging forming process. In this study, the optimization algorithm was used to accurately identify the high-temperature constitutive model parameters of Ti6Al4V titanium alloy, and the hot working diagram was established to optimize the hot forming process parameters. The optimal forming conditions of Ti6Al4V titanium alloy are given. Ti6Al4V alloy was subjected to high-temperature compression tests at 800–1000 °C and at strain rates of 0.01–5 s−1 on a Gleeble-1500D thermal/mechanical simulation machine. Each parameter of the Hansel–Spittel constitutive model was taken as an independent variable, and the accumulated error between the stress calculated by the constitutive model and the stress obtained by experimentation was used as an objective function. Based on response surface methodology, an inverse optimization method for identifying the parameters of the high-temperature constitutive model of Ti6Al4V alloy is proposed in this paper. An orthogonal test design was adopted to obtain sample point data, and a third-order response surface approximate model was established. The genetic algorithm (GA) was applied to reversely optimize the parameters of the constitutive model. To verify the accuracy of the optimized constitutive model, the average absolute relative error (AARE) and correlation coefficient (R) were used to evaluate the reliability of optimized constitutive model. The R value of the model was 0.999, and the AARE value was 0.048, respectively, indicating that the established high-temperature constitutive model for Ti6Al4V alloy has good calculation accuracy. The flow stress behavior of the material could be accurately delineated. Meanwhile, in order to study the formability of Ti6Al4V alloy, the hot processing map of the alloy, based on a dynamic material model, was established in this paper. The optimum hot working domains of the Ti6Al4V alloy were determined within 840–920 °C/0.01–0.049 s−1 and 940–980 °C/0.11–1.65 s−1; the hot processing map was verified in combination with the microstructure, and the fine and equiaxed grains and a large amount of β phase could be found at 850 °C/0.01 s−1.

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Section: Resultsmentioning

confidence: 99%

Constitutive Model Parameter Identification Based on Optimization Method and Formability Analysis for Ti6Al4V Alloy

Chen

Zhang

et al. 2022

Materials

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“…Figure 14a,b are the kernel average misorientation (KAM) map and grain boundaries (GBs) map of the Al-Zn-Mg alloy at 350 °C/0.001 s −1 . The KAM maps can be directly interpreted according to the dislocation density that there is a positive correlation between KAM value and dislocation density [37,38]. For the alloy at 350 °C/0.001 s −1 , section regions within the initial grains have higher KAM values, which indicated the tangled dislocations were more concentrated within the initial grains in the alloy at a lower temperature.…”

Section: Dynamic Recrystallization In the Studied Alloysmentioning

confidence: 99%

Hot Deformation Characteristics and Processing Parameter Optimization of Al–6.32Zn–2.10Mg Alloy Using Constitutive Equation and Processing Map

Xiao

Wang

Huang

et al. 2021

Metals

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Hot compression tests over the temperature range from 350 °C to 500 °C and strain rates range from 0.001 s−1 to 1 s−1 for homogenized Al–6.32Zn–2.10Mg alloy were carried out on a Gleeble-3800 thermal simulation machine to characterize its hot deformation behavior. At the same time, a modified Arrhenius constitutive equation was established to describe the flow behavior of the alloy, whose average absolute error is 2.89%, which proved to have an excellent predictive effect on the flow stress of the alloy. The hot processing map of the alloy was established, and the stability processing parameters were 460–500 °C and 0.01–0.08 s−1. Then, the Z parameter processing map and activation energy processing (AEP) maps were established for further optimization. Eventually, the optimal processing parameters of the alloy was 460–500 °C (0.03–0.08 s−1). Then, the microstructure of specimens was observed using electron backscatter diffraction. Based on the findings the reasonability of the AEP map and Z parameter map was verified. Finally, electron backscatter diffraction (EBSD) techniques were used to analyze the evolution of the grain structure during the deformation process. It was found that dynamic recovery (DRV) was the main softening mechanism of Al–6.32Zn–2.10Mg. Continuous dynamic recrystallization (CDRX) and discontinuous dynamic recrystallization (DDRX) operated together with the increase of strain, but CDRX was confirmed as the dominant DRX mechanism.

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“…Establishing a processing map on the basis of a dynamic material model (DMM) is a significant part for the optimization on parameters of thermal processing and the control on the microstructure [ 27 ]. Based on the theory of DMM [ 28 ], the power loss (P) during thermal deformation of the workpiece consists of two non-separate parts—the power dissipated by plastic processing ( G ) and microstructure evolution ( J ), as follows [ 29 ]:

where

is the stress,

is the strain-rate, G is the power dissipated parameter, and J is the power dissipated parameter.…”

Section: 3d Hot Processing Map Of Cr8 Alloy Steelmentioning

confidence: 99%

“…As the strain increases, the power dissipation efficiency changes in a large range. This is because the stored energy will increase as the dislocation accumulates when strain increases [ 29 ], however, it may be partially consumed by the movement of the dislocation or changes in the microstructure (e.g., dynamic recovery and recrystallization). The area of high dissipation efficiency gradually broadens with increasing temperature.…”

Section: 3d Hot Processing Map Of Cr8 Alloy Steelmentioning

confidence: 99%

The Parameters Identification of High-Temperature Constitutive Model Based on Inverse Optimization Method and 3D Processing Map of Cr8 Alloy Steel

et al. 2021

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As a novel kind of cold roller steel, Cr8 alloy steel has the characteristics of high hardness, high wear resistance and good toughness, which can effectively prolong the service life of the roller that is an important part of the steel rolling mill. How to accurately define the constitutive model parameters of metal materials is the major problem, because it seriously affects the accuracy of numerical simulation results of the roller hot forming process. In the study of Cr8 alloy steel’s thermal deformation behavior of the present paper, the high temperature compression test was done on a Gleebel-1500D thermal/force simulation testing machine. A novel method of parameter identification was proposed based on inverse optimization. The Hansel–Spittel constitutive model was established by using the inverse optimization method. To carry out the verification on the accuracy of the established constitutive model, the predicted flow-stress of constitutive model was made a contrast to the experimental flow-stress, and the standard statistical parameters were also applied to further evaluation. The results showed a relatively high prediction accuracy of the Hansel–Spittel constitutive model based on the inverse optimization algorithm. Meanwhile, to obtain optimal parameters of Cr8 alloy steel in the thermal processing, 3D thermal processing maps concerning strain-rate, strain and temperature were built based on the dynamic material model. According to the 3D processing map, the most adequate thermal processing parameters of Cr8 alloy steel were obtianed as follows: strain 0.2–0.4, strain-rate 0.05–0.005 s−1, temperature 1100–1150 °C.

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Hot deformation behavior and 3D processing maps of AA7020 aluminum alloy

Cited by 87 publications

References 52 publications

Constitutive Model Parameter Identification Based on Optimization Method and Formability Analysis for Ti6Al4V Alloy

Constitutive Model Parameter Identification Based on Optimization Method and Formability Analysis for Ti6Al4V Alloy

Hot Deformation Characteristics and Processing Parameter Optimization of Al–6.32Zn–2.10Mg Alloy Using Constitutive Equation and Processing Map

The Parameters Identification of High-Temperature Constitutive Model Based on Inverse Optimization Method and 3D Processing Map of Cr8 Alloy Steel

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