2019
DOI: 10.1016/j.matchar.2019.109915
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Hot deformation behavior and flow stress modeling of a Ni-based superalloy

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Cited by 59 publications
(17 citation statements)
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“…Zhang et al [ 28 ] used the Artificial Neural Network model and the strain‐compensated Arrhenius model to describe the hot deformation behavior of FGH98 superalloy near the γ′ solvus. In this study, the constitutive modeling of flow stress, the relationship of the peak stress on deformation parameters (temperature and strain rate) can be described as follows [ 5,7 ] εfalse˙=A1σn1exp(QRT)εfalse˙=A2[ sinh(βσ) ]exp(QRT)εfalse˙=A[ sinh(ασp)]nexp(QRT)Z=εfalse˙exp(QRT)=f(σ)where trueε˙ is the strain rate (s −1 ), σ p is the peak stress (MPa), A (s −1 ) and α (MPa −1 ) are materials constants, n is the stress exponent, R is the universal gas constant (8.314 J K −1 mol −1 ), T is the deformation temperature (K), Q is the apparent activation energy for deformation (J mol −1 ), Z is the Zener–Holloman parameter. Taking natural logarithm of both sides of Equation ()–()lnεfalse˙=lnA+n1lnσnormalpQRTlnεfalse˙=lnA+βσnormalpQRT…”
Section: Resultsmentioning
confidence: 99%
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“…Zhang et al [ 28 ] used the Artificial Neural Network model and the strain‐compensated Arrhenius model to describe the hot deformation behavior of FGH98 superalloy near the γ′ solvus. In this study, the constitutive modeling of flow stress, the relationship of the peak stress on deformation parameters (temperature and strain rate) can be described as follows [ 5,7 ] εfalse˙=A1σn1exp(QRT)εfalse˙=A2[ sinh(βσ) ]exp(QRT)εfalse˙=A[ sinh(ασp)]nexp(QRT)Z=εfalse˙exp(QRT)=f(σ)where trueε˙ is the strain rate (s −1 ), σ p is the peak stress (MPa), A (s −1 ) and α (MPa −1 ) are materials constants, n is the stress exponent, R is the universal gas constant (8.314 J K −1 mol −1 ), T is the deformation temperature (K), Q is the apparent activation energy for deformation (J mol −1 ), Z is the Zener–Holloman parameter. Taking natural logarithm of both sides of Equation ()–()lnεfalse˙=lnA+n1lnσnormalpQRTlnεfalse˙=lnA+βσnormalpQRT…”
Section: Resultsmentioning
confidence: 99%
“…Zhang et al [28] used the Artificial Neural Network model and the strain-compensated Arrhenius model to describe the hot deformation behavior of FGH98 superalloy near the γ 0 solvus. In this study, the constitutive modeling of flow stress, the relationship of the peak stress on deformation parameters (temperature and strain rate) can be described as follows [5,7]…”
Section: Arrhenius-type Constitutive Modelingmentioning
confidence: 99%
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“…The true compressive stress–strain data obtained from the isothermal hot compression tests under different strain rates and temperatures can be used to determine the material constants used in the constitutive equation. The Arrhenius equation has been widely employed to describe the relationship between the strain rate, flow stress and temperature, especially at high temperatures, as follows: Z=trueε˙exp()QitalicRT trueε˙=A1σmexp()QRT0.25em()for0.25emlow0.25emstress level trueε˙=A2exp()βσexp()QRT0.25em()for high stress level trueε˙=Asinhitalicασnexp()QRT0.25em()italicfor0.25emitalicall0.25emitalicstress level where Z is the Zener–Hollomon parameter, trueε˙ is the strain rate (s −1 ), n, m, α, β, A 1 , A 2 , and A are constants of the material, α = β/m , σ is the flow stress (MPa), Q is the activation energy of deformation (J/mol), R is the universal gas constant (8.314 J/mol K), and T is the absolute temperature (K).…”
Section: Resultsmentioning
confidence: 99%
“…A processing map is an effective way to optimize the hot‐working parameters and analyze the mechanisms under different deformation conditions, and the processing map has been widely adopted during the hot‐working processes of a variety of metallic materials. [ 8–14 ] Prasad [ 15 ] first presented a processing map, which can be used to predict and analyze the deformation mechanism of materials and optimize reasonable hot deformation parameters to avoid material defects.…”
Section: Introductionmentioning
confidence: 99%