47 citations · 247 across the 20 of their papers we have counts for
6 papers · 1 filter
Deep kernel learning for integral measurements
Carl Jidling, Johannes Hendriks, Thomas B. Schön +1
Deep kernel learning refers to a Gaussian process that incorporates neural networks to improve the modelling of complex functions. We present a method that makes this approach feas…
Neutron Diffraction Strain Tomography: Demonstration and Proof-of-Concept
A. W. T. Gregg, J. N. Hendriks, C. M. Wensrich +2
Recently, a number of reconstruction algorithms have been presented for residual strain tomography from Bragg-edge neutron transmission measurements. In this paper, we examine whet…
Neutron diffraction strain tomography: 2D axisymmetric sample geometry
V. Luzin, A. W. T. Gregg, J. N. Hendriks +1
We consider a method for neutron residual strain/stress analysis combining features of transmission strain tomography (e.g. through Braggs edge imaging) and neutron diffraction tec…
Tomographic Reconstruction of Triaxial Strain Fields from Bragg-Edge Neutron Imaging
J. N. Hendriks, A. W. T. Gregg, R. R. Jackson +6
This paper presents a proof-of-concept demonstration of triaxial strain tomography from Bragg-edge neutron imaging within a three-dimensional sample. Bragg-edge neutron transmissio…
Neutron Transmission Strain Tomography for Non-Constant Stress-Free Lattice Spacing
J. N. Hendriks, C. Jidling, T. B. Schön +3
Recently, several algorithms for strain tomography from energy-resolved neutron transmission measurements have been proposed. These methods assume that the stress-free lattice spac…
A Bayesian Approach to Triaxial Strain Tomography from High-energy X-ray Diffraction
J. N. Hendriks, C. M. Wensrich, A. Wills
Diffraction of high-energy X-rays produced at synchrotron sources can provide rapid strain measurements, with high spatial resolution, and good penetrating power. With an uncollima…