Analytical modeling of contact mechanics of helical gear tooth by considering surface roughness effects
Bibliographic record
Abstract
Release of metal debris from a pair of gear teeth is a consequence of their contact. Any excessive metal debris can lead to the onset of fatigue and failure. This paper aims to derive a contact mechanics-based model to obtain the energy absorption of helical gear teeth. The proposed model includes the roughness effect of teeth contact surfaces. The mean value of the asperity summit curvature, the standard deviation of the asperity height distribution and the area density of the asperity height distribution are the three statistical parameters that describe the micron-scale surface roughness. An explicit approximation is obtained to relate the contact load and the minimum surface separation and to estimate the energy loss. Then an analytical expression is derived for the plastic energy dissipation per cycle as a function of plasticity index for gear teeth. The proposed function can be applied in the design of gears by engineers and manufacturers. Additionally, a pertinent lumped mass at the area of interaction is assumed to describe the contact frequency and damping ratio using a nonlinear dynamic model.
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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 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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".