Tooth Influence on Flexural and Torsional Flexibility, and Model Tooth Number Prediction for Optimum Dynamic Simulation of Wide-Faced Spur Gears
Bibliographic record
Abstract
Refined dynamic analyses of gear pairs, including precise tooth contact description, often lead to unreasonable simulation requirements. Therefore, numerous models employ simplifications, such as two-dimensional deflection of the engaged gear set, which is inappropriate for wide-faced wheels. Other models propose three-dimensional (3D) representation of one tooth on a complete hub. This approach introduces the torsional and flexural deflection of the gear body, but underestimates the corresponding stiffness. Since forthcoming improvements of gear analysis should offer efficient 3D dynamic simulation of wide-faced gear sets, this paper primarily quantifies the flexibility error levels implied with 3D one tooth full hub spur gear models. Subsequently, a procedure is developed to determine the number of teeth required for a 3D model so that it will include the torsional and flexural flexibility of the spur gear body, within acceptable error levels. This procedure offers an efficient approach to optimize the (precision)/(simulation time) ratio. The method deals with gears of any diametral pitch, and covers the common face width and tooth number ranges.
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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 | 0.003 |
| 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 | 0.000 | 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".