Statistical Analysis and Stochastic Dislocation-Based Modelling of Microplasticity

TL;DR

This paper statistically characterizes microscale plasticity and introduces a stochastic dislocation-based model to predict stress-strain behavior, capturing the increased variability in small-scale deformation.

Contribution

It develops a novel stochastic microplasticity model informed by discrete dislocation dynamics simulations, bridging discrete dislocation behavior with continuum stress-strain predictions.

Findings

Model accurately predicts stress-strain curves of micropillars.

Captures the increased scatter in microscale plasticity.

Provides a framework for future microplasticity modeling.

Abstract

Plastic deformation in microscale differs from the macroscopic plasticity in two respects: (i) the flow stress of small samples depends on their size (ii) the scatter of plasticity increases significantly. In this work we focus on the scatter of plasticity. We statistically characterize the deformation process of micropillars under tension using results from discrete dislocation dynamics (DDD) simulations. We then propose a stochastic microplasticity model which uses the extracted information from the above statistical characterization to make statistical predictions regarding the micropillar stress-strain curves. This model aims to map the complex dynamics of interacting dislocations onto a 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 the discrete dislocation dynamics. We compare the model predictions with the underlying DDD simulations and outline potential future applications of the same modelling approach.