Large deformation behavior and energy absorption of rotating square auxetics
Bibliographic record
Abstract
This study focuses on large deformation in-plane response of rotating square (RS) auxetics under quasi-static tension with an emphasis on their energy absorption (EA). First, metallic RS samples were fabricated and tested under uniaxial tension and the results were used to establish validated numerical models using ANSYS. Gurson–Tvergaard–Needleman (GTN) model was employed to evaluate the ductile damage and its capability to predict failure of the RS structure was investigated. Numerical analyses were then conducted to compare the large tensile responses of a regular RS (R-RS) and a bio-inspired RS (Bio-RS-0) introduced by Sorrentino et al. (2022). Subsequently, a parametric study evaluated the effect of the size of a circular perforation in the square region of the Bio-RS-0 on the stress distribution, force–deformation response, failure mechanism, and specific energy absorption (SEA) of the structures. Bio-RS-0 enhanced the SEA of R-RS by more than 250% due to the increased engagement of the geometry in plastic deformation . Large perforations significantly influenced the response in larger strains and their failure mode. Two failure mechanisms were identified which could be adjusted by perforation size. An optimum perforation size and the corresponding failure mode were identified for maximum SEA. Potential applications for energy absorbing auxetics in tension were discussed at the end.
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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 | 0.001 |
| 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 | 0.000 |
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 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".