MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction

Mohammad Samin Nur Chowdhury; Shimin Tang; Singanallur V. Venkatakrishnan; Hassina Z. Bilheux; Gregery T. Buzzard; Charles A. Bouman

arXiv · 2505.22187

MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction

Mohammad Samin Nur Chowdhury·Shimin Tang·Singanallur V. Venkatakrishnan·Hassina Z. Bilheux·Gregery T. Buzzard·Charles A. Bouman

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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.

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Mohammad Samin Nur Chowdhury, Shimin Tang, Singanallur V. Venkatakrishnan, Hassina Z. Bilheux, Gregery T. Buzzard, Charles A. Bouman. 2025-05-28. MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction. https://arxiv.org/abs/2505.22187

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