Mechanical characterization of auxetic stainless steel thin sheets with reentrant structure
Bibliographic record
Abstract
Abstract Smart materials in auxetic form present a great potential for various medical applications due to their unique deformation mechanisms along with durable infrastructure. Both analytical and finite element (FE) models are extensively used in literature to characterize mechanical response of auxetic structures but these structures are mostly thick enough to be considered as bulk material and 3D inherently. Auxetic plates in very thin form, a.e. foil, may bring numerous advantages such as very light design and better biodegradability when needed. However, there is a gap in literature on mechanical characterization of auxetic thin plates. In this study, structural analysis of very thin auxetic plates under uniaxial loading is investigated using both FE method and experimental method. 25 μ m thick stainless steel (316L) plates are fabricated with reentrant texture for three different unit cell dimensions and tested under uniaxial loading using universal testing machine. 25 and 50 μ m thick sheets with same cell dimensions were analyzed using implicit transient FE model including strain hardening and failure behaviors. FE results cover all the deformation schemes seen in actual tests and total deformation level matches with test results. Effect of plate thickness and cell geometry on auxetic behavior is discussed in detail using FE results. Finally, based on FE analysis results, an optimum geometry for prolonged auxetic behavior, high flexibility and high durability is suggested for future potential applications.
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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 | 0.001 | 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".