Data-driven prediction of strain fields in auxetic structures and non-contact validation with mechanoluminescence for structural health monitoring

Abstract

Recent advancements in 3D printing technology have significantly enhanced the potential of auxetic structures, which are notable for their negative Poisson’s ratio, particularly in applications such as sensor technology and structural health monitoring. Central to the performance of these structures is the accurate estimation of the effective strain parameter, a critical metric for assessing structural integrity. However, as structural complexity increases, estimating this parameter becomes increasingly challenging. The fabrication and real-world validation of these structures are equally important challenges. This paper introduces two key innovations for the practical application of auxetic structures. First, we present a multi-kernel hierarchical deep neural network that leverages finite element simulation data to accurately predict effective strain fields in complex auxetic configurations. This model architecture not only reduces the number of parameters requiring training but also enhances feature learning and generalization capabilities, achieving over 90% accuracy in predicting strain fields. Second, we validate these predictions using a 3D-printed specimen embedded with mechanoluminescent (ML) particles. This approach enables direct, non-contact visualization of strain in real-time, offering high spatial and temporal resolution. The alignment observed between predicted and observed strain concentration areas demonstrates the efficacy of integrating ML technology into auxetic designs. This integration significantly improves the reliability and diagnostic capabilities of advanced structural systems.