Quick Iteration Algorithm for Cylindrical Gear Contact Pattern Development
Bibliographic record
Abstract
Gear load is transmitted by the engaged gear teeth. The load distribution on the engaged tooth surfaces directly determines gear tooth stress status and service life. The contact pattern, gear tooth contact pressure distribution, directly alters the maximum contact and bending stresses, is critical for drive system designs. For a real gear train, the deformations of gear blank, bearing, housing, etc. contribute to contact pressure distribution. It consequently changes local contact pressure and bending stress. To accurately predict the gear contact pressure and bending stress in service, the interaction of whole gear train components needs to be modeled in the gear tooth contact analysis. It is not computational efficient for general purpose FEA packages. Transmission3D (Calyx) is designed for gear contact pattern simulation for given gear tooth surface geometry. When the contact pattern deviates from the design target, how to design a new gear tooth geometry is an open issue and discussed in this paper. Inverse engineering concept with Boussinesq solution is invoked and implemented through Excel macro for gear tooth surface microgeometry design. The design iteration based on this developed method is fast and low cost for gear pattern development.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.006 |
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".