MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866916764175040512 |
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| author | Chowdhury, Mohammad Samin Nur Tang, Shimin Venkatakrishnan, Singanallur V. Bilheux, Hassina Z. Buzzard, Gregery T. Bouman, Charles A. |
| author_facet | Chowdhury, Mohammad Samin Nur Tang, Shimin Venkatakrishnan, Singanallur V. Bilheux, Hassina Z. Buzzard, Gregery T. Bouman, Charles A. |
| contents | 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. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2505_22187 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction Chowdhury, Mohammad Samin Nur Tang, Shimin Venkatakrishnan, Singanallur V. Bilheux, Hassina Z. Buzzard, Gregery T. Bouman, Charles A. Image and Video Processing Signal Processing 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. |
| title | MONSTR: Model-Oriented Neutron Strain Tomographic Reconstruction |
| topic | Image and Video Processing Signal Processing |
| url | https://arxiv.org/abs/2505.22187 |