Enhancing helmet pressure sensing with advanced 3D printed gyroid architectures

• 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 culminates in the creation of a smart helmet that effectively detects critical pressure changes, advancing protective headgear technology.

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Publication Details

Journal
Materials & Design
Published
2024-12-11
DOI
https://doi.org/10.1016/j.matdes.2024.113535
Citations
7
Primary Topic
Aerospace Engineering and Energy Systems
Type
article
Field-Weighted Citation Impact
9.09

Funders

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article

Enhancing helmet pressure sensing with advanced 3D printed gyroid architectures

Woo Soo Kim, Danielle Jaye S. Agron, Chao Bao, Edwin L. Thomas et al.
7 citations
Materials & Design
Aerospace Engineering and Energy Systems
9.09
article

Enhancing helmet pressure sensing with advanced 3D printed gyroid architectures

Woo Soo Kim, Danielle Jaye S. Agron, Chao Bao, Edwin L. Thomas, Taeil Kim, Chris Vattathichirayi
article en
7 citations

Abstract

• 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 culminates in the creation of a smart helmet that effectively detects critical pressure changes, advancing protective headgear technology.

Materials & DesignVol. 249
Baylor University (US), Simon Fraser University (CA), Texas A&M University (US)
Natural Sciences and Engineering Research Council of Canada
Affordable and clean energy
Openalex Percentile: Top 3%
Aerospace Engineering and Energy Systems
9.09
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