Transmission error modeling and analysis of herringbone gear drive based on the measured manufacturing errors
Bibliographic record
Abstract
Transmission error (TE) is an important index affecting the positioning accuracy and dynamic performance of gear drive. An original discrete slice model for TE analysis of herringbone gear drive based on the measured manufacturing errors (MEs) is proposed, involving tooth surface error, cumulative pitch error, and alignment error of double helical surfaces. In the model, herringbone gears are discretized into a series of slices to deal with the three-dimensional contact problem. The measured cumulative pitch error and alignment error are described as the position error of slices, while the measured tooth surface error is constructed as the real surface shape by B-spline method. Thus, the measured MEs are converted into the contact gaps of slices. The compatibility and equilibrium equations under error and load conditions are derived from the kinematic geometry and elastic mechanics. Based on the discrete slice model, the relationship between measured ME and TE is established explicitly. The ME of herringbone gear drive is measured, the effects of measured ME on TE under no-load and load conditions were analyzed, and TE test of herringbone gears was carried out. The test results are consistent with the properties and values of simulations, which verifies the proposed 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".