A LOW COST MACRO-MICRO POSITIONING SYSTEM WITH SMA-ACTUATED MICRO STAGE
Bibliographic record
Abstract
Macro-micro systems allow high-resolution positioning over greater ranges of operation that would be achievable with precision positioning systems. Piezoceramic actuators have established themselves as the principle technology for commercial micro-positioning applications, and the trend in research is to push the limits of resolution down to the nanometer and sub-nanometer scales. Other smart materials offer the potential for lightweight, continuous actuation over small ranges, and hence may be useful in micro-positioning applications. This work focuses on the potential for SMA actuators to enable low-cost micro-positioning. Compared to piezos, SMA offer longer range and lower actuation voltages, enabling lower-cost drive electronics and removing the need for costly precision mechanical amplification stages. A prototype single-axis macro-micro positioning system is described, with a macro range of 200 mm and relative positioning precision of better than 5 5μm. The micro stage is driven by an NM70 SMA actuator from NanoMuscle. Macro and micro stages are modelled and controllers developed, and experimental system performance is evaluated. The success of the system provides an inexpensive platform for the study of macro-micro positioning issues such as stage coupling, friction, and drive flexibility, as well as for the position control of SMA.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".