Simulation of Stochastic Discrete Dislocation Dynamics in Ductile Vs Brittle Materials

TL;DR

This paper uses 2D discrete dislocation dynamics simulations to compare dislocation behavior in ductile aluminum and brittle tungsten, introducing a nonlocal transport model for dislocation motion based on machine learning.

Contribution

It presents a novel nonlocal transport model for dislocation dynamics, incorporating machine learning to parameterize the model from simulation data.

Findings

Dislocation velocities and interactions differ significantly between Al and W.

The nonlocal transport model accurately captures stochastic dislocation motion.

Machine learning effectively determines parameters for the nonlocal operators.

Abstract

Defects are inevitable during the manufacturing processes of materials. Presence of these defects and their dynamics significantly influence the responses of materials. A thorough understanding of dislocation dynamics of different types of materials under various conditions is essential for analyzing the performance of the materials. Ductility of a material is directly related with the movement and rearrangement of dislocations under applied load. In this work, we look into the dynamics of dislocations in ductile and brittle materials using simplified two dimensional discrete dislocation dynamics (2D-DDD) simulation. We consider Aluminium (Al) and Tungsten (W) as representative examples of ductile and brittle materials respectively. We study the velocity distribution, strain field, dislocation count, and junction formation during interactions of the dislocations within the domain. Furthermore, we study the probability densities of dislocation motion for both materials. In mesoscale, moving dislocations can be considered as particle diffusionm, which are often stochastic and super-diffusive. Classical diffusion models fail to account for these phenomena and the long-range interactions of dislocations. Therefore, we propose the nonlocal transport model for the probability density and obtained the parameters of nonlocal operators using a machine learning framework.