Discovering chiral auxetic structures with near-zero Poisson's ratio using an active learning strategy

Abstract

In this paper, a set of hexachiral auxetic structural designs with near zero Poisson's ratio (ZPR) characteristics is discovered via the combination of machine learning and experimentally validated finite element simulation. An active learning-enhanced Gaussian process model is utilized to generate multiple designs with near-ZPR properties and discover the boundary of the positive and negative Poisson's ratio. The results show that active learning successfully constructs a probabilistic estimation of the ZPR boundary. A comprehensive analysis of the identified ZPR contour is performed to extract crucial design insights. The findings indicate that the near-ZPR characteristic can be attained through various combinations of geometric parameters. This offers users the flexibility to select the configuration that best aligns with their specific requirements. Additionally, an investigation of the various ZPR configurations that have been discovered is carried out to understand the mechanism that yields near-ZPR property. One discovered near-ZPR design was subsequently fabricated using 3D printing for validation purposes. The experimental outcomes demonstrated a good agreement with the numerical predictions, underscoring the effectiveness of the active learning strategy in uncovering designs that closely approach ZPR conditions.