Anisotropic self-adaptive digital volume correlation with optimal cuboid subvolumes

Conventional digital volume correlation (DVC) approaches all use cubic subvolumes for full-field internal displacement mapping. In practical applications, however, real test samples would undergo heterogeneous deformations in three directions due to anisotropic material properties, complex microstructures and/or nonuniform boundary conditions. Based on the self-adaptive DVC (SA-DVC) algorithm we developed recently, here we propose a novel anisotropic self-adaptive DVC (ASA-DVC) approach using cuboid subvolumes and the first-order shape function to realize accuracy-enhanced 3D internal deformation measurement. We first briefly introduce the V-shaped theoretical error model as a function of subvolume size to facilitate optimal subvolume size identification in x, y and z directions. Then, the basic framework of the presented ASA-DVC method capable of automatically specifying optimal subvolume size in three directions at each calculation point is described. Finally, both numerically simulated and real-world experiments are employed to demonstrate the accuracy advantages of the ASA-DVC over routine DVC. (paper)