Enhancing helmet pressure sensing with advanced 3D printed gyroid architectures

作者：Chao Bao, Danielle Jaye S. Agron, Taeil Kim, Chris Vattathichirayi, Edwin L. Thomas, Woo Soo Kim · 发表于：Materials & Design · 年份：2024 · DOI：10.1016/j.matdes.2024.113535 · 被引用次数：10 · 研究领域：Aerospace Engineering and Energy Systems、Tactile and Sensory Interactions、Aerodynamics and Fluid Dynamics Research

• The representative elementary volume simulation model was carried out to minimize the complexities of 3D printed structures. • A gyroid structure with double hollow struts showed exceptional strength and energy absorption capabilities. • A smart helmet was designed with pressure sensing ability by the embedded gyroid sensor. The gyroid structure, known for its exceptional strength and energy absorption, is ideal for 3D printing applications due to its self-supporting capability. Existing simulation models often overlook the complexities of the 3D printing process, leading to discrepancies between isotropic models and empirical data. To address this, we introduce a representative elementary volume (RVE) simulation model to accurately represent the fused layers from the Fused Deposition Modeling (FDM) process. By establishing Young’s modulus of the fused layer at 48.7 % of pure matrix material, we enhance the model’s accuracy to align with experimental data. We explore energy buffering within the triply periodic minimal surface (TPMS) gyroid model. A new design featuring a thin gyroid TPMS structure with double hollow struts improves energy absorption while enhancing overall efficiency. Additionally, we develop a G slab-based capacitive pressure sensor using advanced robotic 3D printing technology, achieving an impressive pressure sensitivity of 78.43 MPa −1 in the range of 0–0.060 MPa, with a sensitivity of 13.72 MPa −1 at operational pressures up to 0.181 MPa. This culminat...