Robust Inference of Two-Dimensional Strain Fields from Diffraction-based Measurements

TL;DR

This paper introduces a robust algorithm for inferring two-dimensional strain fields from diffraction measurements that respects physical constraints, improving accuracy over traditional interpolation methods especially with large gauge volumes.

Contribution

It presents a novel numerically robust method for strain field inference from diffraction data that enforces equilibrium and loading conditions, outperforming standard interpolation techniques.

Findings

The algorithm accurately reconstructs strain fields from simulated data.

Experimental results confirm improved accuracy over natural neighbour interpolation.

Method performs well with large gauge volumes and shorter beam-times.

Abstract

Diffraction-based methods have become an invaluable tool for the detailed assessment of residual strain and stress within experimental mechanics. These methods typically measure a component of the average strain within a gauge volume. It is common place to treat these measurements as point measurements and to interpolate and extrapolate their values over the region of interest. Such interpolations are not guaranteed to satisfy the physical properties of equilibrium and applied loading conditions. In this paper, we provide a numerically robust algorithm for inferring two dimensional, biaxial strain fields over a region of interest from diffraction-based measurements that satisfies equilibrium and any known loading conditions. By correctly treating the measurements as gauge volume averages rather than point-wise the algorithm has better performance when large gauge volumes and subsequently shorter beam-times are used. This algorithm is demonstrated on simulation and experimental data and compared to natural neighbour interpolation with linear extrapolation and is shown to provide a more accurate strain field.