Flexural–torsional modal interaction in MEMS actuators initiated by minuscule asymmetry
Bibliographic record
Abstract
Abstract An efficient actuation technique for electrostatic MEMS actuators exploiting electro-mechanical-mechanical modal interactions is proposed. The flexural–torsional equations of motion are established, and we manifest that the initiation of a 2:1 autoparametric modal interaction between in-plane bending and torsional modes of the actuator that is supposed to be symmetrical with respect to its axis of rotation is contingent upon the presence of a quadratic stiffness term, which arises from the existence of non-zero first moments of area of the actual cross-section in prismatic microbeams. In order to efficiently reduce the AC voltage value required to reach the activation of the 2:1 mechanical modal interaction, the electrical resonant frequency is syntonized to half of the natural frequency of the in-plane bending mode. The results indicate that the amplitude of the in-plane motion saturates upon the initiation of an energy exchange between the bending and torsional motions. Through suitable tuning of the AC frequency, the amplitude of the in-plane motion is minimized, while the amplitude of the torsional motion, the indirectly excited mode, is maximized. Our results demonstrate that the actuator's torsional motion, when subjected to a 1:2:1 electro-flexural–torsional modal interactions, is triggered by applying a maximum voltage of 10 V, resulting in about 20 degrees rotational angle. Furthermore, prolific frequency combs are generated as a result of secondary Hopf bifurcations along the large-amplitude response branches, inducing quasi-periodicity in the MEMS dynamics.
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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".