An ease-off tooth surface redesign for spiral bevel gears considering misalignment under actual working conditions
Bibliographic record
Abstract
To improve the loaded performance of spiral bevel gears, a novel tooth surface redesign method considering misalignment is proposed based on ease-off. First, the digital features of the contact pattern were extracted, and the equivalent misalignment was obtained by an optimal method according to the minimum deviation of the contact path. Second, a pinion target surface whose performance under misalignment was consistent with the original gear in standard position was built, and a pinion surface with good meshing performance under misalignment was redesigned with equivalent misalignment. Third, the flank modification was carried out to cut down on the loaded transmission error of gear under misalignment. Through simulations, it is found the transmission error and the contact path of redesign gear considering misalignment were the same as original gear in standard position. The loaded transmission error amplitude of original gear under misalignment was 43.91% higher than original gear in standard position, and the loaded transmission error amplitude of redesign gear after optimization under misalignment was 44.73% lower than original gear in standard position and 61.60% lower than original gear under misalignment. The tooth surface stress of redesigned gear after optimization under misalignment was also significantly improved. This proposed redesign method, which considers misalignment on the basis of ease-off, can greatly improve the loaded meshing quality of gear under actual working conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".