MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction

TL;DR

MONSTR is a novel algorithm that reconstructs 2D residual strain tensors from neutron Bragg edge measurements using a multi-agent consensus framework, enabling high-quality results with limited data.

Contribution

It introduces the MONSTR algorithm, a model-oriented approach leveraging consensus equilibrium for tensor reconstruction from neutron strain tomography data.

Findings

High-quality tensor reconstruction from limited measurements

Effective use of multi-agent consensus framework

Demonstrated with simulated data

Abstract

Residual strain, a tensor quantity, is a critical material property that impacts the overall performance of metal parts. Neutron Bragg edge strain tomography is a technique for imaging residual strain that works by making conventional hyperspectral computed tomography measurements, extracting the average projected strain at each detector pixel, and processing the resulting strain sinogram using a reconstruction algorithm. However, the reconstruction is severely ill-posed as the underlying inverse problem involves inferring a tensor at each voxel from scalar sinogram data. In this paper, we introduce the model-oriented neutron strain tomographic reconstruction (MONSTR) algorithm that reconstructs the 2D residual strain tensor from the neutron Bragg edge strain measurements. MONSTR is based on using the multi-agent consensus equilibrium framework for the tensor tomographic reconstruction. Specifically, we formulate the reconstruction as a consensus solution of a collection of agents representing detector physics, the tomographic reconstruction process, and physics-based constraints from continuum mechanics. Using simulated data, we demonstrate high-quality reconstruction of the strain tensor even when using very few measurements.