Additive manufactured 3D re-entrant auxetic structures for enhanced impact resistance
Bibliographic record
Abstract
Abstract This study presents a novel exploration of the geometric parameters within a 3D re-entrant auxetic lattice structure, specifically focusing on their unique impact energy absorption properties, which were systematically evaluated through drop weight impactor testing. Each lattice configuration was additively manufactured using stereolithography, allowing for precise control over strut thickness (t), re-entrant angle (θ), and the aspect ratio (h/l) of unit cells during both low and high energy impact scenarios. This study found that the overall auxetic behavior is predominantly controlled by the aspect ratio of the cell ribs, while the modulus is governed by rib thickness. A finite element model was subsequently developed to simulate the experimental impact loading conditions and was used to examine a wider range of parameters that were not experimentally tested. The simulated dynamic test results displayed the deformation trends and changes to the Poisson’s ratio. Among the studied parameters, experimental results highlighted that a lattice structure with t = 1.6 mm, θ = 65°, and a h/l ratio = 1.8 exhibited the highest specific energy absorption (SEA) under uniaxial impact deformation with 5 Joules of impact energy. Conversely, when employing 20 Joules of impact energy revealed the greatest SEA at t = 1.0 mm, θ = 65°, and an h/l ratio of 2.2. The results demonstrate unique deformation mechanism of auxetic structures under impact loading and the capacity to adapt the 3D re-entrant lattice structure for applications requiring tailored impact energy absorption.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".