Parrot Beak‐Inspired Metamaterials with Friction and Interlocking Mechanisms 3D/4D Printed in Micro and Macro Scales for Supreme Energy Absorption/Dissipation
Bibliographic record
Abstract
Energy absorption and dissipation features of mechanical metamaterials have widespread applications in everyday life, ranging from absorbing shock impacts to mechanical vibrations. This article proposes novel bioinspired friction‐based mechanical metamaterials with a zero Poisson's ratio behavior inspired from parrot's beaks and manufactured additively. The mechanical performances of the corresponding metamaterials are studied at both macro and micro scales by experiments and finite element analysis (FEA). An excellent agreement is observed between the FEA and both microscopic and macroscopic scale experiments, showing the accuracy of the developed digital tool. Performances are compared to traditional triangular lattice metamaterials. Both experimental tests and FEA results demonstrate the following advantages: 1) absorbing and dissipating energy per unit of mass (SEA) at large compressive strains without global buckling; 2) bistable deformation patterns including friction‐based and interlocking mechanisms; 3) reversible deformation patterns after unloading; 4) shape recovery behavior after a heating–cooling process; and 5) the higher elastic modulus of micro metamaterials compared with their macro counterparts. This is the first demonstration of a bioinspired friction‐based design of 3D‐printed mechanical metamaterials that feature absorbing/dissipating energy, stability, and reversibility properties to cater to a wide range of sustainable meta ‐cylinders in micro and macro scales.
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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 | 0.000 |
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".