Hot Deformation Characteristics—Constitutive Equation and Processing Maps—of 21-4N Heat-Resistant Steel

Li, Yiming; Ji, Hongchao; Li, Wangda; Li, Yaogang; Wei, Pei; Liu, Jinping

doi:10.3390/ma12010089

Cited by 22 publications

(15 citation statements)

References 39 publications

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“…Huang et al, reported a maximum hot compressive stress value of 350 MPa with material developed through cast product [38]. Similar results of 350 MPa were reported by Li et al, and Ji et al, for 21-4N material developed through the cast process [19,20]. This can be attributed to the fact that inhomogeneous distribution of grain growth from the surface to inner layers occurred during uneven solidification rate.…”

Section: Hot Compression Measurementssupporting

confidence: 58%

“…This non-equilibrium process works based on energizing and quenching, whereby the alloyed materials are brought into the metastable state with the application of pressure and temperature. Moreover, studies related to hot compression for 21-4N were carried out previously using the casting route [18][19][20], whereas hot compression studies of 21-4N steel developed using the powder metallurgy route have yet to be reported.…”

Section: Introductionmentioning

confidence: 99%

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Characterization of Microstructure and High Temperature Compressive Strength of Austenitic Stainless Steel (21-4N) through Powder Metallurgy Route

et al. 2022

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Exposure of the engine valve to high temperatures led to the degradation of the valve material due to microstructural instability and deteriorating mechanical properties. Performance enhancement and alteration in microstructures can be attained through the powder metallurgy route which is a viable method to produce near net shape components. In this current study, the development of austenitic stainless steel (21-4N) through the powder metallurgy route as an alternate material for engine valves was investigated. Mechanical alloying was carried out for the pre-alloyed mixtures and consolidated using vacuum hot pressing. Sintering parameters were fixed at 1200 °C, 50 MPa and at a vacuum level of 10-3 Torr. A scanning electron microscope was used to analyze the morphology of the milled powders. Densities for the hot pressed powders were compared with theoretical densities and found to be around 98–99%. Observations regarding grain size, the presence of austenitic grain, heterogeneous distribution of metal carbides and analysis of chemical composition along the metal matrix were determined using both optical and electron microscopes. X-ray diffraction was carried out for both the consolidated and powder samples. The hot pressed samples exhibited a hardness value of 410 ± 10 Hv. An isothermal compression test for the sintered samples was carried out at a temperature of 650 °C and strain rate of 0.001 s−1. It is showed that the compressive strength of 1380 MPa. An analysis between the room temperature yield strength obtained from hardness measurement and the strengthening mechanism based on the microstructure was conducted. Grain size, dislocation and solid solution are the major strengthening mechanisms which strengthen the material. Overall, the development of valve steel material through the powder metallurgy route exhibited improved metallurgical and mechanical properties in comparison to the corresponding cast product.

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Section: Hot Compression Measurementssupporting

confidence: 58%

Section: Introductionmentioning

confidence: 99%

Characterization of Microstructure and High Temperature Compressive Strength of Austenitic Stainless Steel (21-4N) through Powder Metallurgy Route

et al. 2022

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“…Finally, the sample was compressed at high temperature with different  and  . After test, water quenching immediately [35][36] .…”

Section: Methodsmentioning

confidence: 99%

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel During High Temperature Deformation

Li¹,

Ji²,

Cai³

et al. 2019

Preprint

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The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ) of 0.01-10s-1, the deformation temperature (T) of 1273-1453K and the maximum deformation is 0.916. The data of the stress-strain ( - )were obtained. Based on the - data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and , it has a fairly high prediction accuracy. And it can predict flow stress ( ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a FEM of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

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“…Finally, the sample was compressed at high temperature with different ε and ε˙. After test, water quenching immediately [44,45].…”

Section: Methodsmentioning

confidence: 99%

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation

Cai

et al. 2019

Materials

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The Gleeble-1500D thermal simulation test machine was used to conduct the isothermal compression test on 21-4N at the strain rate ( ε ˙ ) of 0.01–10 s−1, the deformation temperature (T) of 1273–1453 K and the maximum deformation is 0.916. The data of the stress-strain (σ-ε) were obtained. Based on the σ-ε data, the Johnson-Cook (J-C), modified J-C, Arrhenius and Back-Propagation Artificial Neural Network (BP-ANN) models were established. The accuracy of four models were verified, analyzed and compared. The results show that J-C model has a higher accuracy only under reference deformation conditions. When the deformation condition changes greatly, the accuracy of J-C model is significantly reduced. The coupling effect of T and ε ˙ of modified J-C model is considered, and the prediction accuracy is greatly improved The Arrhenius model introduces Zener-Hollomon (Z) to represent the coupling effect of T and ε ˙ , it has a fairly high prediction accuracy. And it can predict flow stress (σ) accurately at different conditions. The accuracy of BP-ANN model is the highest, but its learning rate is low, the learning and memory are unstable. It has no memory for the weights and thresholds of the completed training. So, there are certain limitations of it in use. Finally, a Finite Element Method (FEM) of the isothermal compression experiment for four models were established, and the distribution of the equivalent stress field, equivalent strain field and temperature field with the deformation degree of 60% were obtained.

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Hot Deformation Characteristics—Constitutive Equation and Processing Maps—of 21-4N Heat-Resistant Steel

Cited by 22 publications

References 39 publications

Characterization of Microstructure and High Temperature Compressive Strength of Austenitic Stainless Steel (21-4N) through Powder Metallurgy Route

Characterization of Microstructure and High Temperature Compressive Strength of Austenitic Stainless Steel (21-4N) through Powder Metallurgy Route

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel During High Temperature Deformation

Comparative Study on Constitutive Models for 21-4N Heat Resistant Steel during High Temperature Deformation

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