When the elastic properties of structured materials become direction-dependent, the number of descriptors of their elastic properties increases. In two-dimensions (2D), for example, an anisotropic material can be described by up to 6 independent constants, as opposed to 2, when the properties are direction-independent. Such high number of independent elastic constants expands the design space of structured materials, and leads to unusual phenomena, such as materials that can twist under uniaxial compression. However, this increased number of independent parameters also makes the experimental evaluation of anisotropic material properties more challenging.
In this talk, we will present an experimental technique to evaluate the 6 independent elastic constants in 2D structured materials, from a single experiment. The technique combines digital image correlation, unsupervised machine learning, and a multi-axis load cell in a mechanical testing machine, to extract all 6 independent elastic constants, using displacements and global force data.