Experimental mechanical properties, nonlinear bending and instability analysis of 3D-printed auxetic tubular metastructures using multiscale finite element and Ritz methods
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Bibliographic record
Abstract
In the field of medical technology, specialized equipment utilizes tubular metastructures with a negative Poisson’s ratio in vascular stents to reduce the risk of embolism. The deliberate incorporation of auxetic structures into stent design offers several benefits over traditional stents. This study examines the nonlinear instability and bending responses of reentrant perfect and imperfect 3D-printed tubular metastructures. First, the governing equations for the auxetic tube with geometric imperfections under transverse and axial mechanical loads are derived using the von-Kármán nonlinear assumption and Timoshenko theory. Then, the equations are derived using the principle of virtual displacement. The physical characteristics of the reentrant structure are derived using Malek-Gibson relations, while tensile tests with digital image correlation (DIC) are used to determine the physical properties of polylactic acid (PLA). Scanning electron microscopy (SEM) images help investigate variations in modulus of elasticity and ultimate tensile strength in dogbone specimens, and the Ritz method with Chebyshev polynomials is employed to discretize the nonlinear equations. Second, two numerical algorithms are used to analyze the static behavior of the metatube within the nonlinear framework. The validation study is conducted for the auxetic tube and the representative volume element (RVE) of the unit cell based on the literature and finite element software Abaqus. Following the validation of the mathematical model, an extensive investigation is conducted to assess how differing parameters impact the mechanical bending and nonlinear instability analysis of the reentrant tube.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 0.000 |
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 it