Influence of different combinations of high-order topological modification on contact pattern and load distribution of helical gear
Bibliographic record
Abstract
An integrated model is proposed to evaluate the contact pattern and load distribution of helical gear with different combinations of high-order topological modification, which is established combining the finite element method (FEM), the analytical method (AM), and the nonlinear programming method. Based on the elastomer contact model of tooth surface, the FEM is used to separate the flexural-shear deformation of the teeth, while the contact AM is introduced to derive the local contact deformation. The contact characteristics of gears can be finally obtained by solving the nonlinear programming model with the constraints of deformation and load and finally verified by the finite element analysis. The results show that the longitudinal crowning can significantly improve the load distribution and the edge contact of the tooth. When the modified curve becomes a parabola of higher order, the contact characteristics change obviously. The pressure of double-crowned (4) gears was uniform and the amplitude decreased, but the amplitude of pressure of double-crowned (6) gears increases and the variance of load sharing ratio is more obvious at the beginning of the middle meshing period.
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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 | 0.000 | 0.001 |
| 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 | 0.001 | 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".