A Novel Stick-Slip Nanopositioning Stage Integrated with a Flexure Hinge-Based Friction Force Adjusting Structure
Bibliographic record
Abstract
The piezoelectrically-actuated stick-slip nanopositioning stage (PASSNS) has been applied extensively, and many designs of PASSNSs have been developed. The friction force between the stick-slip surfaces plays a critical role in successful movement of the stage, which influences the load capacity, dynamic performance, and positioning accuracy of the PASSNS. Toward solving the influence problems of friction force, this paper presents a novel stick-slip nanopositioning stage where the flexure hinge-based friction force adjusting unit was employed. Numerical analysis was conducted to estimate the static performance of the stage, a dynamic model was established, and simulation analysis was performed to study the dynamic performance of the stage. Further, a prototype was manufactured and a series of experiments were carried out to test the performance of the stage. The results show that the maximum forward and backward movement speeds of the stage are 1 and 0.7 mm/s, respectively, and the minimum forward and backward step displacements are approximately 11 and 12 nm, respectively. Compared to the step displacement under no working load, the forward and backward step displacements only increase by 6% and 8% with a working load of 20 g, respectively. And the load capacity of the PASSNS in the vertical direction is about 72 g. The experimental results confirm the feasibility of the proposed stage, and high accuracy, high speed, and good robustness to varying loads were achieved. These results demonstrate the great potential of the developed stage in many nanopositioning applications.
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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 | 0.000 |
| 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".