| We develop speed, efficiency, and accuracy improvements to a three-dimensional (3D) digital volume correlation (DVC) algorithm, which measures displacement and strain fields throughout the interior of a material. Our goal is to perform DVC with resolution comparable to that achieved in 2D digital image correlation, in time that is commensurate with the image acquisition time. This represents a significant improvement over the current state-of-the-art available in the literature. Using an X-ray micro-CT scanner, we can resolve features at the 5 micron scale, generating 3D images with up to 36 billion voxels. We utilize linear and quadratic shape functions with tricubic spline interpolation to achieve high accuracy. We improve the algorithm’s speed and robustness through an improved coarse search, efficient implementation of spline interpolation, and using smoothing splines to address noisy image data. For DVC, the volume of data, number of correlation points, and work to solve each correlation point all grow cubically. We therefore employ parallel computing to handle this tremendous increase in computational and memory requirements. We study how various parameters affect the accuracy of the solution, and how to refine the solution to achieve improved accuracy at reduced computational cost. We demonstrate the effectiveness of our improved DVC implementation using simulated deformations of 3D micro-CT scans of polymer and ceramic foam samples. |
