Role Played by Grain Boundaries in Plastic Deformation of Polycrystalline Metals: A Discrete Dislocation Dynamics Study

2020 ◽  
pp. 187-194
Author(s):  
Tawqeer Nasir Tak ◽  
Aditya Prakash ◽  
Indradev Samajdar ◽  
P. J. Guruprasad
Keyword(s):  
Plastic Deformation ◽  
Grain Boundaries ◽  
Dislocation Dynamics ◽  
Discrete Dislocation Dynamics ◽  
Polycrystalline Metals ◽  
Discrete Dislocation

Stress-Driven Migration of Low-Angle Grain Boundaries Near Crack Tips in Nanocomposites Containing Incoherent Nanoinclusions

2018 ◽  
Vol 55 (1) ◽  
pp. 26-34
Author(s):  
S.V. Bobylev ◽  
L.-S.D. Galeeva
Keyword(s):  
Grain Boundaries ◽  
Metal Matrix ◽  
Critical Stress ◽  
Dislocation Dynamics ◽  
Metal Matrix Nanocomposites ◽  
Discrete Dislocation Dynamics ◽  
Discrete Dislocation ◽  
Growing Crack ◽  
Crack Tips ◽  
Matrix Nanocomposites

Abstract Theoretical model describing stress-driven migration of low-angle grain boundaries (GBs) in the vicinity of growing crack in metal matrix nanocomposites with reinforcing (metallic or ceramic) incoherent nanoinclusions is proposed. Using two-dimensional discrete dislocation dynamics approach profiles of migrating GBs are analytically calculated and critical stress for transition into unstable migration mode is found. It is shown that the presence of crack always promotes stress-driven migration and thus grain growth.

A study of the submicron indent-induced plastic deformation

1999 ◽  
Vol 14 (6) ◽  
pp. 2251-2258 ◽  
Author(s):  
C. F. Robertson ◽  
M. C. Fivel
Keyword(s):  
Plastic Deformation ◽  
Three Dimensional ◽  
Dislocation Nucleation ◽  
Dislocation Dynamics ◽  
Dynamics Simulation ◽  
Nanoindentation Test ◽  
Experimental Conditions ◽  
Discrete Dislocation Dynamics ◽  
Discrete Dislocation ◽  
Transmission Electron

A new method has been developed to achieve a better understanding of submicron indent-induced plastic deformation. This method combines numerical modeling and various experimental data and techniques. Three-dimensional discrete dislocation dynamics simulation and the finite element method (FEM) were used to model the experimental conditions associated with nanoindentation testing in fcc crystals. Transmission electron microscopy (TEM) observations of the indent-induced plastic volume and analysis of the experimental loading curve help in defining a complete set of dislocation nucleation rules, including the shape of the nucleated loops and the corresponding macroscopic loading. A validation of the model is performed through direct comparisons between a simulation and experiments for a nanoindentation test on a [001] copper single crystal up to 50 nm deep.

Statistical analysis and stochastic dislocation-based modeling of microplasticity

2015 ◽  
Vol 24 (3-4) ◽  
pp. 105-113 ◽  
Author(s):  
Olga Kapetanou ◽  
Vasileios Koutsos ◽  
Efstathios Theotokoglou ◽  
Daniel Weygand ◽  
Michael Zaiser
Keyword(s):  
Plastic Deformation ◽  
Complex Dynamics ◽  
Dislocation Dynamics ◽  
Small Samples ◽  
Plastic Behavior ◽  
Stochastic Description ◽  
Discrete Dislocation Dynamics ◽  
Discrete Dislocation ◽  
Plastic Deformation Behavior ◽  
Macroscopic Plasticity

AbstractPlastic deformation of micro- and nanoscale samples differs from macroscopic plasticity in two respects: (i) the flow stress of small samples depends on their size, and (ii) the scatter of plastic deformation behavior increases significantly. In this work, we focus on the scatter of plastic behavior. We statistically characterize the deformation process of micropillars using results from discrete dislocation dynamics (DDD) simulations. We then propose a stochastic microplasticity model that uses the extracted information to make statistical predictions regarding the micropillar stress-strain curves. This model aims to map the complex dynamics of interacting dislocations onto stochastic processes involving the continuum variables of stress and strain. Therefore, it combines a classical continuum description of the elastic-plastic problem with a stochastic description of plastic flow. We compare the model predictions with the underlying DDD simulations and outline potential future applications of the same modeling approach.

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