A Detailed Investigation of Gear Body-Induced Tooth Deflections and Development of an Improved Analytical Solution
Bibliographic record
Abstract
Many researchers have developed analytical methods to evaluate gear meshing stiffness. Some of these methods ignored the effect of the gear body while others used a simplified model to consider its effect. Until now, a detailed investigation of gear body-induced tooth deflections has been rare, especially for the double-tooth-pair meshing period. In this study, we present a detailed investigation of gear body-induced tooth deflections. To be specific, we will discuss how to accurately evaluate gear body-induced tooth deflections using the finite element analysis, and what are the effect of parameters such as loading and gear parameters on gear body-induced tooth deflections. Then, an improved solution is developed for evaluating the body-induced tooth deflection. In the single-tooth-pair meshing period, the improved formula is developed based on a popular formula proposed by Sainsot and Velex. This is achieved by optimizing the coefficients used in their formula to make the formula more accurate to evaluate gear body-induced tooth deflections. Meanwhile, we introduce a new term called affiliated body stiffness to evaluate the body-induced tooth deflections in the double-tooth-pair meshing period. The improved method can give higher accuracy in evaluating gear body-induced tooth deflections of spur gears with a pressure angle of 20°.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".