Influences of cooling rates on delta ferrite of nuclear power 316H austenitic stainless steel

Li, Xin; Gao, Fei; Jiao, Junhong; Cao, Guangming; Wang, Yong; Liu, Zhenyu

doi:10.1016/j.matchar.2021.111029

Cited by 23 publications

(8 citation statements)

References 32 publications

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“…This indicates that part of the δ ‐ferrite underwent a δ ‐ferrite/ γ ‐austenite solid‐state transformation at this temperature. [ 10,15 ] With the increment of the Si content, the δ ‐ferrite/ γ ‐austenite transformation temperature gradually decreased, which were 1279.3, 1263.6, and 1231.3 °C. As the temperature decreased, additional γ ‐austenite nucleated and grew at the δ ‐ferrite grain boundaries or inside δ ‐ferrite via solid‐state transition.…”

Section: Resultsmentioning

confidence: 99%

“…Confocal scanning laser microscopy (CSLM) is a powerful method for investigating the solidification processes of low-carbon steels, stainless steels, and metallic glasses. [9][10][11][12] Because C is regarded as an austenite-forming element, increasing its content in X10CrAlSi18 FHSS may facilitate austenite transition during the solidification. [13] As a result, using CSLM to examine the microstructure evolution of X10CrAlSi18 FHSS during the solidification process may not only give in situ data support but also provide a theoretical foundation for accomplishing solidification structure control.…”

Section: Introductionmentioning

confidence: 99%

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Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Zhang,

Li,

Guan

et al. 2023

steel research int.

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Herein, the δ‐ferrite/γ‐austenite transformation and the precipitation behavior of M23C6 carbides in X10CrAlSi18 ferritic heat‐resistant stainless steel (FHSS) with various Si contents at a cooling rate of 100 °C min−1 using confocal scanning laser microscopy (CSLM) are investigated. The findings reveal that γ‐austenite preferentially forms along the δ‐ferrite phase boundaries, and it progressively precipitates into the δ‐ferrite phase as the temperature decreases. The increase in the Si content reduces the δ‐ferrite/γ‐austenite transformation temperature. It also inhibits the martensite transformation in the subsequent cooling process, decreasing the volume fraction of γ‐austenite/martensite. M23C6 carbides are mostly found at the δ‐ferrite and γ‐austenite/martensite phase boundaries. Meanwhile, the nucleation of M23C6 carbides becomes more difficult as the volume fraction of γ‐austenite/martensite decreases. Furthermore, the complex solidification mechanism of the nucleus is addressed.

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Section: Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Zhang,

Li,

Guan

et al. 2023

steel research int.

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“…2 °C during solidification. Li et al [ 18 ] studied the volume fraction variation of as‐cast δ‐ferrite at a cooling rate from 5 to 6000 °C min −1 (100 °C s −1 ) of 316H austenitic stainless steel and found that the solidification mode of 316 H was a ferrite–austenite (FA) type regardless of the cooling rate. The effects of the cooling rate on the solidification and microstructure evolution in the SAF 2205 DSS were studied using the differential scanning calorimetry (DSC) method and it was reported that the content of δ‐ferrite increased with an increase in cooling rate.…”

Section: Introductionmentioning

confidence: 99%

Combination of In Situ Confocal Microscopy and Calorimetry to Investigate Solidification of Super‐ and Hyper‐Duplex Stainless Steels

Wang

Sukenaga

Shibata

et al. 2023

steel research int.

Self Cite

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The solidification processes of super‐ and hyper‐duplex stainless steels (i.e., UNS S32750 and S 33 207) are investigated in situ by a high‐temperature confocal laser scanning microscope (HT‐CLSM) and differential scanning calorimetry (DSC). The variations of δ‐ferrite phase fraction during solidification are measured quantitatively. The results show that liquid L→δ‐ferrite transformation first occurs at a certain degree of supercooling during the solidification process of steel. UNS S3DSS 3207 with a higher Cr content can result in a higher nucleation temperature and faster growth of δ‐ferrite compared to those of UNS S332750 steel. Moreover, both the liquidus (TL) and solidus temperatures (TS) are increased with the increasing Cr content, while TL increases greater than TS. Electron microscopies are used to quantify the fraction and composition of each phase. Scheil equation is employed to predict the distribution behavior of the main alloying elements in the solidification process, and the predicted results are consistent with the experimental findings. This study aims to provide real‐time experimental insights into the solidification kinetics of state‐of‐the‐art high‐alloy‐grade duplex steels and benefits for controlling the casting process in the real production of stainless steels.

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“…Despite the stable development of new metallurgical directions such as additive manufacturing [1,2], amorphous [3,4] and composite structuring [5,6] technologies, the majority of critical steel products in the nuclear industry in the coming decades will be manufactured using the traditional scheme, including steel melting and casting, hot and cold plastic deformation and mechanical and heat treatment [7][8][9]. However, the requirements for the microstructure and properties of the materials will be permanently increased.…”

Section: Introductionmentioning

confidence: 99%

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

Churyumov

Kazakova

Churyumova

2022

Metals

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Hot forming is an essential part of the manufacturing of most steel products. The hot deformation behaviour is determined by temperature, strain rate, strain and chemical composition of the steel. To date, constitutive models are constructed for many steels; however, their specific chemical composition limits their application. In this paper, a novel artificial neural network (ANN) model was built to determine the steel flow stress with high accuracy in the wide range of the concentration of the elements in high-alloyed, corrosion-resistant steels. The additional compression tests for stainless Cr12Ni3Cu steel were carried out at the strain rates of 0.1–10 s−1 and the temperatures of 900–1200 °C using thermomechanical simulator Gleeble 3800. The ANN-based model showed high accuracy for both training (the error was 6.6%) and approvement (11.5%) datasets. The values of the effective activation energy for experimental (410 ± 16 kJ/mol) and predicted peak stress values (380 ± 29 kJ/mol) are in good agreement. The implementation of the constructed ANN-based model showed a significant influence of the Cr12Ni3Cu chemical composition variation within the grade on the flow stress at a steady state of the hot deformation.

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Influences of cooling rates on delta ferrite of nuclear power 316H austenitic stainless steel

Cited by 23 publications

References 32 publications

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Combination of In Situ Confocal Microscopy and Calorimetry to Investigate Solidification of Super‐ and Hyper‐Duplex Stainless Steels

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

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Influences of cooling rates on delta ferrite of nuclear power 316H austenitic stainless steel

Cited by 23 publications

References 32 publications

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M23C6 Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M23C6 Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Combination of In Situ Confocal Microscopy and Calorimetry to Investigate Solidification of Super‐ and Hyper‐Duplex Stainless Steels

Modelling of the Steel High-Temperature Deformation Behaviour Using Artificial Neural Network

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Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel

Effect of Si on δ‐Ferrite/γ‐Austenite Transformation and M₂₃C₆ Precipitation During Solidification of X10CrAlSi18 Ferritic Heat‐Resistant Stainless Steel