Automated detection of deformation mechanisms in re-entrant honeycomb auxetics using machine learning

The intricate geometrical configuration of an auxetic structure enables high energy dissipation capacity at the expense of a highly nonlinear mechanical response. Under external stimuli, complicated deformation mechanisms emerge which dictate the extent of energy dissipation. Recently, the new ‘plastic hinge tracing’ method (Dhari et al., 2021) was introduced to detect such deformation mechanisms for elastoplastic porous materials. This approach is however subjective and cumbersome since it requires monitoring several plastic regions in consecutive deformed configurations. The present study innovatively extends this method by implementing machine learning (ML) techniques for objective detection of deformation modes in auxetics. To this end, a logistic regression ML model was developed to classify the deformation modes of a re-entrant honeycomb structure. The proposed procedure could successfully detect four out of the six deformation modes (‘X’, distorted ‘X’, ‘V’, and ‘V + Z + V’) using the training datasets generated by the finite element analysis and image labels created by K-means clustering algorithm. The success of the proposed automated approach lays the foundation for identifying the deformation mechanisms of other auxetics and porous materials with plastic deformations.