Mastering the art of designing mechanical metamaterials with quasi-zero stiffness for passive vibration isolation: a review
Bibliographic record
Abstract
Abstract This review serves as a comprehensive design strategy for designing quasi-zero stiffness (QZS) mechanical metamaterials (MMs). It discusses their underlying deformation mechanisms that enable the attainment of QZS behavior under both compressive and tensile loadings. While the QZS characteristic of metamaterials has garnered considerable attention, further research is essential to unlock their potential fully. Numerous QZS metamaterials have been meticulously reviewed. They comprise various elements and mechanisms, including positive and negative stiffness elements (PS and NS), PS elements with variable stiffness, bending mechanisms employing stiff joints/areas, buckling, buckling-rotating, and bending/buckling deformation mechanisms leading to a QZS feature. Furthermore, the capability of multi-material, adaptive, smart metamaterials, origami (bending around the hinge of the folded joints), and kirigami lattices (out-of-plane buckling via cutting patterns) are weighted. These diverse mechanisms contribute to achieving QZS behavior in metamaterials under both compression and tension loads, which is paramount for various mechanical applications such as passive vibration isolation. This review effectively categorizes QZS metamaterials based on their underlying mechanisms, providing scholars with valuable insights to identify suitable mechanisms for the desired QZS feature.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".