Nonlinear Sensitivity Analysis of Twin Non-Symmetric Micro-Cantilever Beams
Bibliographic record
Abstract
In this paper, the dynamic performance of two parallel micro-cantilever beams is investigated and the results are presented. The dynamic response is of high interest in MEMS structures as it is related to the performance of the micro-devices. The micro-cantilever beams can be easily fabricated and yield high sensitivity to variations of physical quantities. In this work, the dynamic response of two parallel flexible cantilever beams subjected to a difference of potential is analyzed. This configuration was modeled as mass-damper spring systems with two degrees of freedom. Such a system can be used to measure the viscosity of liquids. This viscosity is related to damping between two masses representing the two beams in the discrete system model. The fabrication of two identical beams using MEMS fabrication processes may be difficult as the fabrication process may yield some variabilities. Thus, the two beams may be slightly different which will be reflected in their mass and stiffness. This condition was assumed in the proposed model. As the system is sensitive to the applied difference of potential such that the pull-in voltage represents a good indicator of the sensitivity performance. The dynamic analysis was carried out at potentials close to the pull-in value. Stability of the system was evaluated and the responses of the beams were calculated at a potential close to the pull-in voltage. The sensitivity of the system was calculated for different viscosities of liquid between two beams. It was found that an increase of the viscosity yields higher nonlinearity and consequently loose of accuracy while assuming linear stiffness for the beams. In this research, the stiffness of micro-cantilever beams was calculated from small deflection theory of beams. However, there are other methods that could be considered to evaluate the stiffness of the beams. One of this different methods was considered and the sensitivity of the modeled stiffness is discussed. Since the stiff nonlinear differential equations cannot be solved analytically, the numerical approach was exploited. In this work ISODE method from Maple software was used to solve the model described by the two differential equations.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".