Modelling of recalescence effect on austenite decomposition

Guo, Lei; Roelofs, H.; Lembke, M; Bhadeshia, H. K. D. H.

doi:10.1080/02670836.2017.1282693

Cited by 10 publications

(5 citation statements)

References 39 publications

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“…Latent heat was released proportional to the change in phase volume fraction. Guo et al studied the overall phase transformation kinetics based on Avrami theory 18 , and their models have been modified to define a volume fraction of the transformed phase as shown in Eq. ( 9 ).…”

Section: Theoretical Approachmentioning

confidence: 99%

A theoretical approach for estimating the effect of water-jet quenching on low-carbon steel beams

Koo

2021

Sci Rep

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Quenching is an efficient manufacturing technique to improve the strength of steel after hot rolling. The benefit of this application is to enhance the mechanical properties of steel products while reducing strengthening alloying elements, e.g., C, Mn, V, Nb, and N. Quenching and self-tempering (QST) especially for H-beams is a unique material strengthening process that adopts intensive surface cooling and self-tempering. A methodological difficulty in estimating the quenching effect has been a long-standing concern in the QST application. The purpose of this study was therefore to specify quenching parameters, quantify quenching, analyze the effect, and verify the credibility of the results. Transient quenching was simulated in ANSYS to analyze heat transfer and phase transformations due to quenching. An individual concept, e.g., heat exchange, cumulative quenching infiltration, or recalescence phenomena, was merged and interpreted newly for the quenching simulation. Computational results based on theoretical approaches were well consistent with empirical studies.

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Section: Theoretical Approachmentioning

confidence: 99%

A theoretical approach for estimating the effect of water-jet quenching on low-carbon steel beams

Koo

2021

Sci Rep

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“…Two Si-containing steels (Table 1) were used to produce the bainitic microstructure, the choice of these alloys was because of an ongoing study on high-strength bainitic steel [12–14]. Cylindrical samples () were heat-treated in a THERMECMASTOR-Z thermal mechanical simulator under a vacuum of about 10 −3 Pa, with the radial dilatation recorded by a laser precision measuring device.…”

Section: Methodsmentioning

confidence: 99%

Modelling of size distribution of blocky retained austenite in Si-containing bainitic steels

Guo

Roelofs

Lembke³

et al. 2018

Materials Science and Technology

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A model for estimating the blocky retained austenite size distribution in Si-containing steels has been developed for the first time, based on the geometric partitioning of prior austenite grains by bainite sheaves. A random volume allocation method for the two new compartments formed by the formation of one bainite sheaf is adopted for reasons detailed in the text. Random selection of the compartment for subdivision is also employed at each step. The model has been verified experimentally using Si-containing bainitic steels.

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“…During the quenching process of the large-sized forged block, diffusion-controlled (austenite to ferrite) and non-diffusive type (martensitic) transformations take place in the material. In the case of diffusion-controlled transformation, the volume fraction of the transformed phase was calculated using Scheil's additivity rule and the Avrami equation [11,15,18]. Specifically, this consisted of dividing the non-isothermal process of quenching into many isothermal segments and then using the TTT diagram of the steel, the fraction transformed was calculated for each segment.…”

Section: Prediction Of Phase Fractionmentioning

confidence: 99%

“…The maximum possible transformed volume fraction, f max , was also calculated based on a TTT diagram generated by JMatPro ® . The volume fraction of the non-diffusive type transformation (i.e., martensite) was calculated using the Koistinen-Marberger relation [12,15,19]:…”

Section: Prediction Of Phase Fractionmentioning

confidence: 99%

“…A combination of Avrami [11] model, Koistinen-Marburger (K-M) equation [12] and Scheil additivity rule [11][12][13][14][15] have been used to predict phase transformation start and finish temperatures, residual austenite, bainite and martensite transformations as well as grain size variations and carbon content during the cooling process. However, little information is available on the influence of chemical heterogeneity and grain size variation on the volume fraction of the phases during the cooling process of large-size forged blocks [10,13].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Numerical Simulation of Water Quenching of Large Size Steel Forgings: Effects of Macrosegregation and Grain Size on Phase Distribution

Lyassami

Shahriari

Fredj

et al. 2018

JMMP

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In this paper, water quenching of large ingots was simulated using FORGE NxT 1.1 ® Finite Element code. Simulations were carried out for as-forged medium-carbon low-alloy steel. A novel method is proposed to simulate the different parts of a large size forged block with different chemical compositions and grain sizes using the multiple materials method. The effects of macrosegregation, grain size variation and cooling rate on phase distribution through the volume of the forged block were investigated. The delay in transformation kinetics, which is due to the effect of grain size variation and carbon content, was analyzed. Results show that macrosegregation and grain size variations significantly influence transformation start points and the volume fraction of phases that are present in each location of the forged ingot. The proposed prediction method was validated using high-resolution dilatometry experiments and X-ray diffraction measurements to evaluate accurately the volume fraction of martensite, bainite and the percentage of retained austenite for each condition.

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Modelling of recalescence effect on austenite decomposition

Cited by 10 publications

References 39 publications

A theoretical approach for estimating the effect of water-jet quenching on low-carbon steel beams

A theoretical approach for estimating the effect of water-jet quenching on low-carbon steel beams

Modelling of size distribution of blocky retained austenite in Si-containing bainitic steels

Numerical Simulation of Water Quenching of Large Size Steel Forgings: Effects of Macrosegregation and Grain Size on Phase Distribution

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