Influence of Similarity Law on Movement Characteristics of Vibration Actuator
Bibliographic record
Abstract
Currently, a belt conveyor or an automatic guided vehicle is used as a conveying device in a factory. However, Transportation of the ceiling surface and the wall surface in these devices is impossible. Elevator is used for conveying from the lower floor to the upper floor. Therefore, the conveying device capable of movement on a wall and a ceiling significantly improves the efficiency of work. Based on this background, development of working robots capable of transporting on a wall surface is required. In the present study, the vibration actuator with a very simple structure capable of movement on a magnetic substance by means of the inertial force of a vibration model was again considered. Furthermore, upsizing for the actuator in order to improve the propulsion characteristics was considered. The volume of the permanent magnet constituting the vibration component of the actuator was increased according to the similarity rule. Four models of actuator with approximately equal drive frequency were prototyped and experimentally tested. The experimental results demonstrate that the maximum efficiency of the actuator for a standard model pulling its own weight was 27.1 %. Furthermore, the actuator is able to pull a load mass of 170 g. For the actuator of 5 times model, the actuator is able to climb upward at 9.5 mm/s while pulling a load mass of 3500 g. This actuator is able to propel a load mass of approximately 36 times the weight of the actuator itself.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".