The kinetics of precipitation from supersaturated solid solutions

Lifshitz, I.M.; Slyozov, V.V.

doi:10.1016/0022-3697(61)90054-3

Cited by 7,807 publications

(4,112 citation statements)

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“…[24] The tertiary γ′ precipitates were observed to increase in size during aging, Figure 3, approximately following a t 1/3 relationship, as would be expected from classical Lifshitz-Slyozov-Wagner (LSW) theory. [35][36][37][38] However, using this relationship and extrapolating the coarsening of the tertiary γ′ to 1000 hours, the predicted size would remain below that of the secondary γ′ precipitates. This confirms that the absence of tertiary γ′ after 1000 hours of aging is due to resolution of the precipitates, as opposed to coarsening to a size sufficient to merge with that of the secondary γ′.…”

Section: A Precipitate Morphologymentioning

confidence: 99%

Gamma Prime Precipitate Evolution During Aging of a Model Nickel-Based Superalloy

Goodfellow

Galindo-Nava

Christofidou

et al. 2017

Metall Mater Trans A

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The microstructural stability of nickel-based superalloys is critical for maintaining alloy performance during service in gas turbine engines. In this study, the precipitate evolution in a model polycrystalline Ni-based superalloy during aging to 1000 hours has been studied via transmission electron microscopy, atom probe tomography, and neutron diffraction. Variations in phase composition and precipitate morphology, size, and volume fraction were observed during aging, while the constrained lattice misfit remained constant at approximately zero. The experimental composition of the γ matrix phase was consistent with thermodynamic equilibrium predictions, while significant differences were identified between the experimental and predicted results from the γ′ phase. These results have implications for the evolution of mechanical properties in service and their prediction using modeling methods.

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Section: A Precipitate Morphologymentioning

confidence: 99%

Gamma Prime Precipitate Evolution During Aging of a Model Nickel-Based Superalloy

Goodfellow

Galindo-Nava

Christofidou

et al. 2017

Metall Mater Trans A

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“…Most of the published work on modelling of the precipitation kinetics in aluminium alloys is based on two main approaches [1][2][3][4][5][6][7][8][9][10]. In one of the approaches, the modelling of the nucleation, growth and impingement of precipitates are based on the concepts of the Johnson-Mehl-AvramiKolmogorov (JMAK) model [14,15,16] and the modelling of the coarsening kinetics is based on the Lifshitz-Slyozov-Wagner (LSW) theory [17,18]. In the other approach the Kampmann and Wagner (KW) type numerical model [19] is used to predict the complete precipitation kinetics from the nucleation to the coarsening stages.…”

Section: Introductionmentioning

confidence: 99%

A model for precipitation kinetics and strengthening in Al–Cu–Mg alloys

Khan

Starink

Yan

2008

Materials Science and Engineering: A

133

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A physically-based numerical model is developed to predict the microstructural evolution and strengthening in Al-Cu-Mg alloys during isothermal treatments. The modelling of the formation kinetics of the precipitates is based on the Kampmann and Wagner model. The strengthening by the shearable Cu:Mg co-clusters is modelled on the basis of modulus strengthening mechanism and the strengthening by the non-shearable S phase precipitates is based on the Orowan looping mechanism. The model predictions are verified by comparing with the strength and differential isothermal calorimetery data on 2024-T351 aluminium alloys. The microstructural development and strength predictions of the model are generally in good agreement with the experimental data.

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“…These regions can contain non-dissolved intermetallic complexes Al-Y of various sizes. Because of the Ostwald ripening in these regions small crystals or complexes dissolve, and redeposit into larger crystals or complexes [10,11,12]. Therefore, we suppose that in these high Y density areas the number of the segregated domains decreases, but their characteristic size grow in time.…”

Section: Resultsmentioning

confidence: 99%