Kinematic sensitivity analysis of a novel micro-mechanism for displacement amplification
Bibliographic record
Abstract
This research article presents the design and analysis of a displacement amplification mechanism based on a microelectromechanical system (MEMS). The mechanism, compared to generic displacement mechanisms, is smaller and capable of amplifying input displacement by a factor of 6.8. Finite element analysis (FEA) is performed with commercial software Intellisuite using the extended finite element method (XFEM) technique to verify the analytical results from mathematical models. Kinematic response and kinematic sensitivity analysis of the amplification mechanism are computationally carried out to predict the effect of different geometric parameters on the performance of the proposed mechanism. The analysis predicts that length and angle of flexure are the two key geometric parameters significantly affecting the amplification factor (AF), with length having a direct relationship and angle of flexure having an inverse relationship. A significant increase in the AF is seen for a flexure length up to 550 μm and angle below 5°. Based on the sensitivity analysis, the design is optimized, and geometric parameters are finalized. Modal analysis and dynamic simulations, including direct-integration transient and steady-state modal analysis, are performed on the mechanism under the application of 25 g. The mechanism can be integrated with any conventional actuating mechanism in a microsystem where the amplification of a small displacement at the output is desired.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".