A review of hot deformation behavior and constitutive models to predict flow stress of high-entropy alloys

Savaedi, Zeinab; Motallebi, Reza; Mirzadeh, Hamed

doi:10.1016/j.jallcom.2022.163964

Cited by 174 publications

(54 citation statements)

References 177 publications

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“…Therefore, in [29,30], it is proposed in Expression (2) to use stresses normalized to the shear modulus (σ m /G) or Young's modulus (σ m /E) instead of stresses σ m . In this case, the activation energy drops to the value of the activation energy of self-diffusion, and the value of the exponent n close to 5, according to some authors, will indicate that the mechanism that controls hot deformation is glide and climb of dislocations [31,32]. However, at present, the precise expressions for the temperature dependence of elastic moduli are not known for all materials.…”

Section: Resultsmentioning

confidence: 95%

Study on the Hot Deformation Behavior of Stainless Steel AISI 321

et al. 2022

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In this study, the hot deformation behavior of austenitic Ti-modified AISI 321 steel with a relatively high content of carbon (0.07 wt.%) and titanium (0.50 wt.%) was studied in the temperature range of 1000–1280 °C and strain rates in the range of 0.01–1 s−1. Hot deformation was carried out with uniaxial compression of cylindrical specimens on a Gleeble 3800 thermomechanical simulator. It is shown that the flow stress increased with a decrease in the deformation temperature and an increase in the strain rate. The shape of the stress-strain curves indicates that, at high temperatures and low strain rates, the hot deformation of AISI 321 steel was accompanied by dynamic recrystallization. The passage of dynamic recrystallization was confirmed by microstructural studies. Hyperbolic sine type of constitutive equation with deformation activation energy Q = 444.2 kJ·mol−1 was established by analyzing the experimental flow stresses. The power-law dependences of the critical strain necessary for the onset of dynamic recrystallization and the size of recrystallized grains on the Zener–Hollomon parameter were established. The value of the parameter Z = 5.6 × 1015 was determined, above which the dynamic recrystallization was abruptly suppressed in the steel under study. It is speculated that the suppression of dynamic recrystallization occurs due to dispersed precipitates of titanium carbonitrides.

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Section: Resultsmentioning

confidence: 95%

Study on the Hot Deformation Behavior of Stainless Steel AISI 321

et al. 2022

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“…It is well known that the magnitude of the activation energy Q reflects the degree of deformation difficulty for plasticity deformation and the thermodynamic mechanism of dislocation movement [ 21 , 49 ]. The activation energy of deformation Q for the studied alloy is 177.3 kJ·mol −1 at a strain of 0.69, which is higher than that of common Al-Zn-Mg-Cu alloys [ 21 , 50 ].…”

Section: Resultsmentioning

confidence: 99%

“…The self-diffusion activation energies for Al, Zn, Mg, Li, and Cu are 142, 102, 134, 55, and 197 kJ·mol −1 , respectively [ 51 ]. Therefore, the self-diffusion activation energy value obtained by weighted mean according to its content [ 49 ] is 137.3 kJ·mol −1 . The experimental activation energy is higher than the weighted mean self-diffusion activation energy and the self-diffusion activation energy for Al.…”

Section: Resultsmentioning

confidence: 99%

Hot Deformation Behavior and Microstructure Evolution of a Novel Al-Zn-Mg-Li-Cu Alloy

Zhu

Jiang

et al. 2022

Materials

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Lightweight structural alloys have broad application prospects in aerospace, energy, and transportation fields, and it is crucial to understand the hot deformation behavior of novel alloys for subsequent applications. The deformation behavior and microstructure evolution of a new Al-Zn-Mg-Li-Cu alloy was studied by hot compression experiments at temperatures ranging from 300 °C to 420 °C and strain rates ranging from 0.01 s−1 to 10 s−1. The as-cast Al-Zn-Mg-Li-Cu alloy is composed of an α-Al phase, an Al2Cu phase, a T phase, an η phase, and an η′ phase. The constitutive relationship between flow stress, temperature, and strain rate, represented by Zener–Hollomon parameters including Arrhenius terms, was established. Microstructure observations show that the grain size and the fraction of DRX increases with increasing deformation temperature. The grain size of DRX decreases with increasing strain rates, while the fraction of DRX first increases and then decreases. A certain amount of medium-angle grain boundaries (MAGBs) was present at both lower and higher deformation temperatures, suggesting the existence of continuous dynamic recrystallization (CDRX). The cumulative misorientation from intragranular to grain boundary proves that the CDRX mechanism of the alloy occurs through progressive subgrain rotation. This paper provides a basis for the deformation process of a new Al-Zn-Mg-Li-Cu alloy.

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“…For its great algorithm to solve the problem of nonlinear regression and excellent prediction accuracy of material rheological behavior, machine learning provided a new idea and method for the establishment of material constitutive models. The current machine learning models for flow stress prediction include artificial neural network [15][16][17][18][19], support vector machine regression [20,21] and K-nearest neighbor regression [22], etc.…”

Section: Introductionmentioning

confidence: 99%

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

Qiang

Zhang

Liu

et al. 2022

Mater. Res. Express

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An accurate constitutive model is essential for designing the process of hot precision forging and numerical simulation. Based on the isothermal compression tests of as-extruded HMn64-8-5-1.5 manganese brass alloy at the deformation temperature of 873-1073 K and strain rate of 0.01-10 s-1, the effect of the friction and deformation temperature rise on the flow stress during the hot compression process was analyzed, and the flow stress curves were corrected. Three constitutive models based on the modified Zerilli-Armstrong, Arrhenius, and a back-propagation neural network (BPNN) optimized by the grey wolf optimization (GWO) algorithm (GWO-BPNN) models were established to describe the high-temperature flow stress of this alloy. Meanwhile, the prediction ability of the three models was evaluated by the calculated values of mean absolute percentage error (MAPE) and root mean square error (RMSE). The values of MAPE for the modified Zerilli-Armstrong, Arrhenius, and GWO-BPNN models were computed to be, 3.139 %, 2.448 % and 1.265 %, and the values of RMSE were calculated to be 1.804, 1.482 and 0.467 MPa, respectively. The GWO-BPNN model was with the greatest prediction ability for the flow stress among these models. The GWO algorithm was introduced to optimize the initial weights and thresholds of the BPNN model, and it has good prediction accuracy and better stability. It can better describe the high-temperature flow behavior of HMn64-8-5-1.5 alloy.

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A review of hot deformation behavior and constitutive models to predict flow stress of high-entropy alloys

Cited by 174 publications

References 177 publications

Study on the Hot Deformation Behavior of Stainless Steel AISI 321

Study on the Hot Deformation Behavior of Stainless Steel AISI 321

Hot Deformation Behavior and Microstructure Evolution of a Novel Al-Zn-Mg-Li-Cu Alloy

Prediction of high-temperature flow stress of HMn64–8–5–1.5 manganese brass alloy based on modified Zerilli-Armstrong, Arrhenius and GWO-BPNN model

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