Discrete stochastic model of point defect-dislocation interaction for simulating dislocation climb

McElfresh, Cameron; Cui, Yinan; Dudarev, S. L.; Po, Giacomo; Marian, Jaime

doi:10.1016/j.ijplas.2020.102848

Cited by 32 publications

(8 citation statements)

References 69 publications

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“…How to establish the correlation between microstructure (defect) and deformation mechanism is important and still an unsolved issue since it remains challenging to incorporate the microscopic mechanisms such as atomic movements into the constitutive model for the macroscopic plastic deformation (Hufnagel et al, 2016;Jiang et al, 2020;Lin et al, 2020;Ruan et al, 2022). We can refer to a kind of established method of crystal plasticity model describing the deformation behavior of crystals, which is usually based on the Orowan equation with significant role of dislocation kinetics (McElfresh et al, 2021;Po et al, 2016;Stukowski et al, 2015;Yu et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

A hierarchically correlated flow defect model for metallic glass: Universal understanding of stress relaxation and creep

Hao

Lyu

Pineda

et al. 2022

International Journal of Plasticity

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Section: Introductionmentioning

confidence: 99%

A hierarchically correlated flow defect model for metallic glass: Universal understanding of stress relaxation and creep

Hao

Lyu

Pineda

et al. 2022

International Journal of Plasticity

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“…Early attempts to address climb in DDD were restricted to fewer than three dimensions or segments of pure edge character and were not compatible with a nodal framework that allows mixed character segments [32][33][34][35][36] . DDD simulations which include climb have typically been restricted to simple configurations such as isolated loops or single dislocations rather than networks, even the more sophisticated recent approaches 37 . The kinematic algorithm for climb proposed in the present study operates within a nodal framework and strictly maintains the glide planes of each segment, providing an ability to represent climb for junctions in addition to mixed character segments.…”

Section: Introductionmentioning

confidence: 99%

The kinetics of static recovery by dislocation climb

Kohnert

Capolungo

2022

npj Comput Mater

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The initial microstructure of a wide range of structural materials is conditioned by thermo-mechanical treatments such as hot-working, tempering, or solution annealing. At the elevated temperatures associated with these treatments the dislocation microstructure evolves, usually decreasing in density through a process known as static recovery. Despite its technological relevance, static recovery is not fully characterized from a theoretical standpoint, with even the controlling mechanisms subject to debate. In this study, a climb-enabled discrete dislocation dynamics (DDD) capability is leveraged to explore the kinetics of static recovery in pure Fe when controlled by dislocation climb. Quantitative data from these simulations is used to develop a revised static recovery law, and provides the parameters appropriate for predictive microstructure models in Fe. This law differs from previous analytical derivations invoking climb of dislocations, following the logarithmic trends typical of experimental observations where prior work did not. Direct comparison between the recovery law derived from DDD to experimental recovery data in alpha Fe shows strong agreement across a range of temperatures, and suggests that climb is the controlling mechanism for static recovery in pure metals.

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“…Numerous computational models address the size effect in plasticity responses of microscale metallic materials. High-fidelity discrete models, such as molecular dynamics [141,154,74,71,151] and discrete dislocation dynamics (DDD) [25,27,29,81,89,97,66], explicitly simulate the microstructural evolutions leading to plastic deformation. On the other hand, crystal plasticity [50,54,9,163,115,110,17] and strain gradient plasticity models [5,63,49,65,126,13,12,77,14] relies on a continuum description of the overall microstructural behavior resulting in the size effect phenomena.…”

Section: Introductionmentioning

confidence: 99%

A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty

Tan

Villa

Shamsaei

et al. 2020

Preprint

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Multiscale models of materials, consisting of upscaling discrete simulations to continuum models, are unique in their capability to simulate complex materials behavior. The fundamental limitation in multiscale models is the presence of uncertainty in the computational predictions delivered by them. In this work, a sequential multiscale model has been developed, incorporating discrete dislocation dynamics (DDD) simulations and a strain gradient plasticity (SGP) model to predict the size effect in plastic deformations of metallic micro-pillars. The DDD simulations include uniaxial compression of micro-pillars with different sizes and over a wide range of initial dislocation densities and spatial distributions of dislocations. An SGP model is employed at the continuum level that accounts for the size-dependency of flow stress and hardening rate. Sequences of uncertainty analyses have been performed to assess the predictive capability of the multiscale model. The variance-based global sensitivity analysis determines the effect of parameter uncertainty on the SGP model prediction. The multiscale model is then constructed by calibrating the continuum model using the data furnished by the DDD simulations. A Bayesian calibration method is implemented to quantify the uncertainty due to microstructural randomness in discrete dislocation simulations (density and spatial distributions of dislocations) on the macroscopic continuum model prediction (size effect in plastic deformation). The outcomes of this study indicate that the discrete-continuum multiscale model can accurately simulate the plastic deformation of micro-pillars, despite the significant uncertainty in the DDD results. Additionally, depending on the macroscopic features represented by the DDD simulations, the SGP model can reliably predict the size effect in plasticity responses of the micropillars with below 10% of error.

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Discrete stochastic model of point defect-dislocation interaction for simulating dislocation climb

Cited by 32 publications

References 69 publications

A hierarchically correlated flow defect model for metallic glass: Universal understanding of stress relaxation and creep

A hierarchically correlated flow defect model for metallic glass: Universal understanding of stress relaxation and creep

The kinetics of static recovery by dislocation climb

A Predictive Discrete-Continuum Multiscale Model of Plasticity With Quantified Uncertainty

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