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 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 | 0.001 | 0.000 |
| 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 | 0.000 | 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".