Analytical modeling of an inclined folded-beam spring used in micromechanical resonator devices
Bibliographic record
Abstract
Abstract Accurate estimation of the mechanical behavior of springs is crucial for the proper design of Microelectormechanical systems (MEMS). The main objective of this study is to derive a closed-form equation for the calculation of the stiffness of an inclined spring in the form of folded-beamss. The energy-based method was used to calculate the displacements of a folded-beams that was fixed at one end and giuded at the other end. The analytical model was then compared with the finite element method using ANSYS for different inclination angles of the folded-beams spring, showing good agreement. The angle on inclination has changed from zero to 180 degress, and stiffness of folded-beams is detemined. The derived expressions of the compliances were checked for the case of serpentine springs with inclination angle of zero, and different length ratios against the literature. It is found that neglecting small lengths for calculating the stiffness of the folded-beams spring is not justified. The influential geometrical parameters, including different lengths of the spring and inclination angle of the spring, on the stiffness are studied. It is found that the angle of inclination of the principal axes of spring constants depends on the geometrical parameters, and the angle of inclination has more effect on the stiffness of a folded-beams spring than the number of folds.
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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 | 0.001 | 0.000 |
| 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".