Design of a Novel Vertical Motion Piezoworm Positioner
Bibliographic record
Abstract
Piezoelectric-based positioners are incorporated into stereotaxic devices for microsurgery, scanning tunneling microscopes for the manipulation of atomic and molecular-scale structures, nanomanipulator systems for cell microinjection and machine tools for semiconductor-based manufacturing. This wide range of applications requires the design and development of large load capacity, long stroke and compact positioning stages without compromising high speed and precision. Although several precision positioning systems have been developed for planar motion, most are neither suitable nor readily lend themselves to provide long travel range with large load capacity in vertical axis because of their weights, size, design and embedded actuators. To address the limitations of the traditional technologies, a novel positioner is being developed specifically for vertical axis motion based on a piezoworm arrangement in flexure frames. Analytical calculations and finite element analysis are used to optimize the design of the lifting platform to provide maximum thrust force while maintaining a compact size. To make a stage frame more compact, the actuator is integrated into the stage body which mainly consists of a moveable component outside a rigid frame. The clamps are designed such that no power is needed to maintain a fixed vertical position, holding the payload against the force of gravity.
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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) | 0.002 | 0.001 |
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".