Finding multistable metamaterials chain's continuous force/energydisplacement path to program its functionalities
Bibliographic record
Abstract
Mechanical metamaterials are made to display characteristics that are not present in regular materials. Recent advances in alternative structures, including inclined beams, curved beams, shallow shells, Origamis, and shellulars, have made it possible to attain bistability and multistability as novel features. There are still two unsolved concerns about the use of multistable mechanical metamaterials to create electrical systems, mechanical memories, and deployable structures. First, is it possible to programme mechanical instability? Second, how can we tune mechanical properties of materials at the post-fabrication stage? Understanding the snapping sequences and changes of the elastic energy in multistable metamaterials is essential to finding the answers to these problems. Here, we represent multistable metamaterials as a chain to be transformed into the desired shape by applying a mechanical stimulus at a specific location on the external boundary of the metamaterial. A continuous path with all conceivable configurations and snap-back released energy is found for the snapping chain. It is found that the number of possible configurations depends on the order of the instability forces. We thoroughly elicit the mechanics of continuous force/energy-displacement curves and reconfiguration sequences and demonstrate how the progrmmable snapping chain can be utilized to create mechanical sensors/memories with sampling and data reconstruction functionalities.
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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".