Design, analysis, and experimental investigation of micro-displacement amplification compliant mechanism for micro-transducers
Bibliographic record
Abstract
This paper presents experimental force and buckling analysis of a compliant micro-displacement amplification mechanism fabricated using the commercially available PolyMUMPs process. The proposed mechanism proficiently amplifies displacement, at two output ends, with an optimal amplification factor of 7.2. Buckling analysis revealed that an amplification factor ranging from 2.8 to 11 may be achieved for an input displacement varying from 0.1 to 7.5 µm. Based on the analysis, the optimal value of the amplification factor is found to be 7.2 with an input displacement of 3.5 µm at the operational force of 60 μN having a buckling load factor (BLF) >1. Critical load magnitude is 187 μN having BLF = 1. Buckling occurred when loading exceeded the critical load value, having BLF <1, and the mechanism failed to produce a significant amplification factor. Static analysis showed that stresses produced are within the safe region, and the structural integrity of the mechanism is not compromised having a factor of safety of 1.4. Modal analysis predicted that the natural frequency of the desired mode is 35.47 kHz. Dynamic simulations, under 15 g dynamic load with a frequency range of 30–40 kHz, confirm the possibility of integrating the proposed mechanism with MEMS devices. Parametric optimization comprehends that length and angle are the two major geometric parameters that govern the working range, force, and amplification factor. For input displacements below 1 µm, the amplification factor is even higher, which is highly beneficial for amplifying small displacements. Static, modal, and dynamic analyses of the designed mechanism have been carried out using finite element method based commercial software IntelliSuite®. The experimental results showed that this mechanism can provide the same amplified displacement at two output points and is self-sufficient to be incorporated as an intermediate compliant mechanism for enhancing the output in the case of both static and dynamic micro-devices.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| 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) | 0.000 | 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 teacher head, 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".