Influence of gear parameters on dynamic characteristics of an ultrasonic vibration system
Bibliographic record
Abstract
A spiral bevel gear is subject to a special load in ultrasonic lapping, which is not only a processing object but also a processing tool, it is necessary to study dynamic characteristics of an ultrasonic vibration system. First, the spiral bevel gear is reasonably simplified to a frustum, which is then combined with a horn to form a new type of composite horn. Based on the theory of plane longitudinal wave propagation, the resonance mathematical model of the gear’s ultrasonic vibration system is established, and the frequency equation of the vibration system with gear characteristic parameters is obtained. Second, the frequency and displacement characteristics of an ultrasonic vibration system are analyzed by means of design examples, and the influence of the law of gear characteristic parameters on dynamic characteristics of the vibration system is studied. Finally, ultrasonic lapping and vibration measurement experiments are carried out using two pairs of different hypoid gears. The results show that the change in dynamic characteristics of an ultrasonic vibration system has a large influence on the finished machining quality of the gear. Therefore, it is necessary to consider the gear characteristic parameters when ultrasonic vibration system is designed. The research results provide an accurate theoretical basis for the detailed design of an ultrasonic lapping vibration system.
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 | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".