Full Scale Gear Tooth Bending Fatigue Tests Obtained Early in the Development of a Rotorcraft Transmission
Bibliographic record
Abstract
A new gear testing method is introduced to reduce development cost and time. It allows component-level testing of individual gear meshes and new gear designs. Instrumented gear sets are tested at full load while the rest of gearbox components are still being built. Since the fatigue strength of the gears is determined earlier in the development cycle, design deficiencies are identified and understood earlier. In this new method, an individual gear mesh installed in a stiff facility housing is used to mimic the contact pattern and bending stress demonstrated by the same mesh in an actual aircraft housing. Analytical gear models are used to identify the displacement difference between the stiff test facility and the aircraft housing. The test stand is designed so it can be adjusted accurately to provide gear and pinion positions that are representative of the deflected positions under load in the aircraft housing. A spiral bevel mesh and a split torque double helical reduction stage with multiple meshes are evaluated using the developed method. The contact pattern and strain survey results of the gear meshes are correlated with predicted results.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.002 | 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".