Intragranular Strain Estimation in Far-Field Scanning X-ray Diffraction using a Gaussian Processes

TL;DR

This paper introduces a Gaussian Process-based method for estimating intragranular strain fields in polycrystalline materials from 3D X-ray diffraction data, improving robustness and accuracy over previous techniques.

Contribution

The novel approach integrates Gaussian Processes with physical constraints to enhance strain field reconstruction from scanning-3DXRD data, providing uncertainty quantification and optimized smoothness.

Findings

GP method yields lower errors than algebraic inversion

Reconstruction validated with synthetic and experimental data

Method provides uncertainty estimates for strain fields

Abstract

A new method for estimation of intragranular strain fields in polycrystalline materials based on scanning three-dimensional X-ray diffraction data (scanning-3DXRD) is presented and evaluated. Given an apriori known anisotropic compliance, the regression method enforces the balance of linear and angular momentum in the linear elastic strain field reconstruction. By using a Gaussian Process (GP), the presented method can yield a spatial estimate of the uncertainty of the reconstructed strain field. Furthermore, constraints on spatial smoothness can be optimised with respect to measurements through hyperparameter estimation. These three features address weaknesses discussed for previously existing scanning-3DXRD reconstruction methods and, thus, offers a more robust strain field estimation. The method is twofold validated; firstly by reconstruction from synthetic diffraction data and, secondly, by reconstruction of previously studied tin (Sn) grain embedded in a polycrystalline specimen. Comparison against reconstructions achieved by a recently proposed algebraic inversion technique is also presented. It is found that the GP regression consistently produces reconstructions with lower root mean squared errors, mean absolute errors and maximum absolute errors across all six components of strain.