Solid-solution hardening

Butt, M. Z.; Feltham, P.

doi:10.1016/0001-6160(78)90213-4

Cited by 61 publications

(23 citation statements)

References 20 publications

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“…The mechanistic origins of such plateaus have historically been unknown. Labusch [57,58] suggested that additional energy barriers enter at high temperatures, but without substantial quantification. Zaiser [59] used scaling arguments coming from the theory of flux pinning in superconductors and provided a clearer picture of how the "plateau" arises and its connection to the low-temperature solute strengthening.…”

Section: Introductionmentioning

confidence: 99%

Solute strengthening in random alloys

et al. 2017

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Random solid solution alloys are a broad class of materials that are used across the entire spectrum of engineering metals, whether as stand-alone materials (e.g. Al-5xxx alloys) or as the matrix in precipitatestrengthening materials (e.g. Ni-based superalloys). As a result, the mechanisms of, and prediction of, strengthening in solid solutions has a long history. Many concepts have been developed and important trends identified but predictive capability has remained elusive. In recent years, a new theory has been developed that builds on one historical model, the Labusch model, in important ways that lead to a well-defined model valid for random solutions with arbitrary numbers of components and compositions. The new theory uses first-principles-computed solute/dislocation interaction energies as input, from which specific predictions emerge for the yield strength and activation volume as a function of alloy composition, temperature, and strain-rate. Being a general model for materials that otherwise have a low Peierls stress, it has broad application and has been successfully applied to Al-X alloys, Mg-Al, twinning in Mg alloys, and recently fcc High-Entropy Alloys. Here, the new theory is presented in a general and systematic manner. Approximations and limiting cases that reduce the complexity and facilitate understanding are introduced, and help relate the new model to various physical features present among the historical array of models. The quantitative predictions of the model in the various materials above is then demonstrated.

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Section: Introductionmentioning

confidence: 99%

Solute strengthening in random alloys

et al. 2017

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“…Those models generally employ two mechanisms to describe the yield strength: a strengthening by grain size reduction, the Hall-Petch effect, and the alloying elements' contribution to the material's strength [15,36,37]. Many of those models have used databases with significant amounts of different alloys to employ a linear regression in order to get parameters that determine the effect of alloying elements on the yield strength of the austenitic steel.…”

Section: Methodsmentioning

confidence: 99%

Ab Initio-Based Modelling of the Yield Strength in High-Manganese Steels

Sevsek

Bleck

2018

Metals

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An ab initio-based model for the strength increase by short-range ordering of C-Mn-Al clusters has been developed. The model is based on ab initio calculations of ordering energies. The impact of clusters on the yield strength of high-manganese austenitic steels (HMnS) is highly dependent on the configurational structure of the cells that carbon atoms will position themselves as interstitial atoms. The impact of the alloying elements C, Mn, and Al on the potential and actual increase in yield strength is analyzed. A model for the calculation of yield strengths of HMnS is derived that includes the impact of short-range ordering, grain size refinement, and solid solution strengthening. The model is in good agreement with experimental data and performs better than other models that do not include strengthening by short-range ordering.

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“…The unit activation process of yielding in the KPN model [5][6][7] involves the stress-assisted, thermally-activated detachment of edge-dislocation segments from short rows of closely spaced solute-atom pinning points. This occurs in a manner similar to the kink-pair mode of escape of screw dislocations from Peierls valleys [17] in nominally pure crystals with high intrinsic lattice friction.…”

Section: The Modelmentioning

confidence: 99%

“…An expression for the activation volume, customarily defined as = T [∂(lnγ)/∂τ] T associated with the CRSS τ (Eq. (7)), in terms of θ, is [5] =…”

Section: Figurementioning

confidence: 99%

“…The kink-pair nucleation (KPN) model of plastic flow in solid-solution crystals [5,6], which is essentially a revised and enlarged version of one initially proposed by Feltham [7], has not only satisfactorily accounted for the SSH features noted above (e.g. [4]), but has also proved to be an effective tool to diagnose the nature of solute distribution in solidsolution crystals, whether metallic [8][9][10][11] or non-metallic, [12,13].…”

Section: Introductionmentioning

confidence: 99%

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Onset of local ordering in some copper-based alloys: critical solute concentration vis-a-vis various solutionhardening parameters

2008

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Abstract:The mode of planar distribution of solute atoms in Cu single crystals alloyed with 0.5 to 8.0 at.%Ge has been investigated via the temperature dependence of the critical resolved shear stress of these alloys. It is found that there exists a critical solute concentration ≈ 5 at.%Ge below which the distribution of solute atoms in the crystal is random, and above which some local ordering occurs. This together with such data available in the literature for Cu-Zn, Cu-Al and Cu-Mn alloys, i.e.≈ 27 at. %Zn, 7 at.%Al and 1 at.%Mn, when examined as a function of the size-misfit factor δ = (1/ )(d /d ) of a given binary alloy system, shows that the value of strongly depends on δ; the smaller the magnitude of δ, the greater the value of and vice versa. Also, the value of is found to correlate well with the electron-to-atom ratio ( / ) of the Cu-Zn, Cu-Al, Cu-Ge and Cu-Mn alloys with the solute concentration = . However, no systematic correlation exists between the critical solute concentration for the onset of local ordering and the modulus -mismatch parameter η = (1/G)(dG/d ).

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Solid-solution hardening

Cited by 61 publications

References 20 publications

Solute strengthening in random alloys

Solute strengthening in random alloys

Ab Initio-Based Modelling of the Yield Strength in High-Manganese Steels

Onset of local ordering in some copper-based alloys: critical solute concentration vis-a-vis various solutionhardening parameters

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