Full Three-Dimensional Strain Measurements on Wood Exposed to Three-Point Bending: Analysis by Use of Digital Volume Correlation Applied to Synchrotron Radiation Micro-Computed Tomography Image Data

Abstract

A microscale three‐point bend experiment on wood has been carried out. The full 3D strain field of the microscale wood structure has been determined by use of digital volume correlation, based on reconstructed 3D image data acquired with synchrotron radiation micro‐computed tomography. The wood specimen, which measures 1.57 × 3.42 × 0.75 mm3, was scanned in different load states along the three‐point bend load cycle, from unloaded state to failure. The correlation algorithm is based on a Chebyshev polynomial description of the displacements, which allows a continuous representation of the 3D deformation fields. The methodology of the correlation algorithm is described thoroughly and its performance is tested for a 3D structure that is exposed to a virtual pre‐defined deformation. The performance is tested both for noise free volume data as well as for structures with additive noise content. The performance test shows that the correlation algorithm resolves the applied deformation satisfyingly well. In the real experiment, on wood microstructure, the displacement fields show a structural behaviour that is consistent with what is expected for a specimen exposed to three‐point bend. However, there are also anomalous effects present in the displacement fields that can be coupled to characteristic features in the cellular structure of the wood. Furthermore, 3D strain calculations based on the obtained displacement data shows a concentration of tensile strain in the region where the specimen eventually collapses. The experimental results show that the use of X‐ray‐based tomography with high spatial resolution in combination with digital volume correlation can successfully be used to perform 3D strain measurements on wood, at the microscale.

Analysis of Failure Modes in Fiber Reinforced Concrete Using X-ray Tomography and Digital Volume Correlation

“…µXRT has most commonly been applied on unloaded material samples, but with in-situ devices it is possible to run mechanical tests within a tomograph thus enabling scanning during loading of specimens [3]. Coupling of µXRT with Digital Volume Correlation (DVC) has been applied to analyze the interior structural evolution of an array of materials, e.g., concrete [4], bone [5], wood [6], rock [7] and granular materials [8]. DVC can essentially be considered as an extension of Digital Image Correlation (DIC) applied to tomographic image volumes to calculate full 3D vector displacements, from which tensor strain fields can be derived.…”

High Performance Digital Volume Correlation

“…Regarding the choice of correlation grid spacing, a common practice is to overlap subsets by half the subset size, so a subset size of 41 voxels yields a grid spacing of 20 voxels between correlation points [20,21,22]. If this practice is followed, asymptotically the computational cost depends on the size of the entire region of interest being correlated, independent of the subset size and grid spacing.…”

“…Choices include C 0 trilinear interpolation [12,28,42], C 1 tricubic Hermite interpolation [5,73,79], and C 2 tricubic spline interpolation [8,21]. In 2D DIC, C 4 biquintic spline interpolation has also been used [71].…”

“…Previously, representative volume elements have been determined by using 2D DIC to extract a representative surface element, and then assuming some type of extension into the third dimension [17,45,53]; DVC now offers the opportunity to attain a true 3D representative volume element. DVC is also useful to analyze complex materials, such as bone [5,46,73,79], rock [42], synthetic foams [67], wood [21], sugar [20] and sand grains [35]; as well as complex behaviors, such as material fatigue after repeated loading cycles [10,62], which are difficult to simulate. Thus, although only a relatively small number of researchers have studied DVC, it is a powerful tool with significant future potential.…”

“…Forsberg and Siviour [20] studied compacted sugar, using 32 3 subsets and computing about 6000 points over a 342 × 342 × 380 voxel image. Forsberg et al [21] investigated a three-point bending problem with wood, using tricubic spline interpolation, an FFT for a coarse search, and Newton's method for the fine search. They reduced the x and y resolution by 2 × 2 binning to fit their computational resources, and used 64 3 subsets with a 128 × 768 × 320 voxel image.…”