A speed regulating scheme for air-turbine dental handpieces
Bibliographic record
Abstract
Air-turbine dental handpieces (ATDH) are used in most of dental practices as the main cutting tool. However, the speed of these handpieces is reduced while cutting a tooth; because no speed controller has been provided for them. This speed-reduction decreases the cutting efficiency of air-turbine handpieces. A review of literature indicated no study concerned with designing a speed controller for ATDH. Thus, in this paper, a control scheme is proposed for regulating the speed including `measurement', `actuation', and `controller-design' stages. An accelerometer was employed to capture vibrations of the handpiece; and the frequency associated with the first peak in the spectrum was indicated to represent the speed. An on/off solenoid valve with pulse-width modulation (PWM) technique was employed to regulate the speed through controlling the input pressure to the handpiece. Changing the duty-cycle of the PWM pulses could vary the speed. A process model from the applied duty-cycle to the measured speed was obtained. This process model includes a linear dynamic and a nonlinear static part. Then, a proportional-integral- (PI) controller was selected, and the gains were tuned. The practicality and efficiency of the proposed control scheme with the PI controller was confirmed by experimental results.
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 | 0.001 |
| 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 | 0.000 | 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".