Towards design optimisation of a lifting platform for a piezoworm-driven high precision positioning stage
Bibliographic record
Abstract
Review of literature on nanopositioning techniques reveals the scarcity of specialised vertical nanopositioning platform model and the necessity of its accurate estimation. In this paper, a design optimisation of a moving platform for a high precision positioning stage driven by a piezoworm actuator has been proposed. The design aim is to exploit maximum output force from the actuator, while maintaining a compact size. The proposed platform is mainly consisted of configurable components so as to form a rectangular rigid frame which can be moved in Z-axis with long stroke. A piezoworm actuator, made of at least three piezostacks, is housed inside the platform and holds the platform against the force of gravity at any vertical position. The range of platform motion is long and limited only by its own height, connecting it to the piezoworm motor’s clamping surfaces on both sides. The platform’s capability to utilise the most from the piezoworm output thrust is modelled analytically and optimised through finite element analysis. Due to its compact structure, the platform can be installed in many configurations and its top surface will allow any another device to be mounted on top for a given application.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".