Computation of Burgers Vectors from Elastic Strain and Lattice Rotation Data

TL;DR

This paper presents a theoretical and computational framework for accurately determining Burgers vectors from strain and lattice rotation data in low-dislocation-density materials, validated with simulated and experimental datasets.

Contribution

It introduces a novel, general method for computing Burgers vectors from experimental strain and rotation data, with implementation and validation across multiple techniques.

Findings

Accurately identifies dislocation positions and Burgers vectors from simulated data.

Demonstrates reliable results with BCDI experimental data.

Shows promising results with HR-TKD data despite uncertainties.

Abstract

A theoretical framework for computation of Burgers vectors from strain and lattice rotation data in materials with low dislocation density is presented, as well as implementation into a computer program to automate the process. The efficacy of the method is verified using simulated data of dislocations with known results. A three-dimensional dataset retrieved from Bragg coherent diffraction imaging (BCDI) and a two-dimensional dataset from high-resolution transmission Kikuchi diffraction (HR-TKD) are used as inputs to demonstrate the reliable identification of dislocation positions and accurate determination of Burgers vectors from experimental data. For BCDI data, the results found using our approach show very close agreement to those expected from empirical methods. For the HR-TKD data, the predicted dislocation position and the computed Burgers vector showed fair agreement with the expected result, which is promising considering the substantial experimental uncertainties in this dataset. The method reported in this paper provides a general and robust framework for determining dislocation position and associated Burgers vector, and can be readily applied to data from different experimental techniques.