3D-printed bio-inspired zero Poisson’s ratio graded metamaterials with high energy absorption performance
Bibliographic record
Abstract
Abstract This study aims at introducing a number of two-dimensional (2D) re-entrant based zero Poisson’s ratio (ZPR) graded metamaterials for energy absorption applications. The metamaterials’ designs are inspired by the 2D image of a DNA molecule. This inspiration indicates how a re-entrant unit cell must be patterned along with the two orthogonal directions to obtain a ZPR behavior. Also, how much metamaterials’ energy absorption capacity can be enhanced by taking slots and horizontal beams into account with the inspiration of the DNA molecule’s base pairs. The ZPR metamaterials comprise multi-stiffness unit cells, so-called soft and stiff re-entrant unit cells. The variability in unit cells’ stiffness is caused by the specific design of the unit cells. A finite element analysis (FEA) is employed to simulate the deformation patterns of the ZPRs. Following that, meta-structures are fabricated with 3D printing of TPU as hyperelastic materials to validate the FEA results. A good correlation is observed between FEA and experimental results. The experimental and numerical results show that due to the presence of multi-stiffness re-entrant unit cells, the deformation mechanisms and the unit cells’ densifications are adjustable under quasi-static compression. Also, the structure designed based on the DNA molecule’s base pairs, so-called structure F‴, exhibits the highest energy absorption capacity. Apart from the diversity in metamaterial unit cells’ designs, the effect of multi-thickness cell walls is also evaluated. The results show that the diversity in cell wall thicknesses leads to boosting the energy absorption capacity. In this regard, the energy absorption capacity of structure ‘E’ enhances by up to 33% than that of its counterpart with constant cell wall thicknesses. Finally, a comparison in terms of energy absorption capacity and stability between the newly designed ZPRs, traditional ZPRs, and auxetic metamaterial is performed, approving the superiority of the newly designed ZPR metamaterials over both traditional ZPRs and auxetic metamaterials.
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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) | 0.000 | 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 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".