Two-dimensional patterned hollow structures (2D-PHS) are an advanced class of metamaterials known for their unique mechanical properties and lightweight nature. Comprising a solid matrix with periodically arranged hollows, 2D-PHS effectively reduce material weight while optimizing stress and strain distribution to maintain structural integrity and strength. This precise geometric control offers superior tunability in strength-to-weight ratios, deformability, and stretchability compared to traditional solid materials. These attributes make 2D-PHS particularly valuable in high-performance lightweight systems. In the aerospace industry, these materials could improve components like aircraft wings and fuselage panels by maintaining high strength with reduced weight. Additionally, their excellent fatigue resistance and energy dissipation capabilities make 2D-PHS ideal for applications subjected to repetitive or cyclic stresses, such as biological tissue engineering scaffolds and impact-resistant devices.
In this context, research team from ShanghaiTech University reported an AI-driven material design framework that combines experimental and computational methodologies to enhance two-dimensional patterned hollow structures (2D-PHS). The framework examines critical factors influencing mechanical properties, including the arrangement, size, and shape of hollow structures, and, by applying AI-driven strategies, the algorithm tailor these parameters to meet practical application needs. By integrating computational modeling with experimental testing, the study seeks to boost the mechanical performance of 2D-PHS and expand their use across various engineering domains. The framework resulted in a 4.3% improvement in average stress uniformity and a 23.1% reduction in maximum stress concentrations. The tensile strength of optimized samples increased from an initial average of 5.9 MPa to 6.6 MPa under 100% strain, demonstrating enhanced mechanical resilience.
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