Design and Development of Piezoelectric Inchworm Actuator
Bibliographic record
Abstract
The objective of this research is to present a proof-of-concept design of an inchworm type piezoelectric actuator with output of maximum displacement and force (or power) for shape control and vibration control of adaptive truss structures. The proposed inchworm actuator adopts “pusher” type with frictional clamping mechanisms. It consists of three main components: two clamping or braking devices and one expanding device. The two frictional clamping devices provide alternating braking forces when the center shaft is moving inside the PZT tubal stack and emulates an inchworm, summing small steps to achieve large displacements. Since the development of a robust clamping mechanism is essential to realize the high force capability, a considerable design effort has been focused on optimizing the clamping device to increase the output force. CATIA is used as a platform to model the whole actuator and ANSYS is used to analyze and optimize the performance of the actuator. The complete design of the proposed actuator has been performed using the finite element analysis. The simulation result confirms that the output force of 74 Newton and incremental displacement in each step of 9 micron can be achieved using the proposed actuator. A prototype of actuator has been fabricated and static tests have been performed to validate the simulation results. The dynamic test of the actuator is currently under process and the result of the dynamic test will be subsequently compared with simulation results.
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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 | 0.000 | 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".