Computer-Aided Machine Setting for Lapping Optimization
Bibliographic record
Abstract
During hypoid gears lapping process, a gear set is running at varying operating positions and under a light load in order to lap the complete tooth surface. The pinions and gears are lapped in pairs. Hence, their tooth surfaces are not only cutters but also workpieces. In the contact region, the contact pressure and sliding speed are different from point to point. This makes lapping to be a very complicated abrasive wear process. So far, knowledge about the relationship between the removed materials and the lapping time as well as how to optimize the lapping process is quite limited. An algorithm was presented (Jiang et al., 2008, “Simulation of Hypoid Gear Lapping,” ASME J. Mech. Des., 130(11), p. 112601) to determine the wear coefficient k for the lapping process of hypoid gears. With the obtained wear coefficient k, a methodology for simulating the lapping process was proposed. Based on the wear coefficient obtained, this work presents a computer-aided machine setting procedure to optimize the lapping cycle in order to improve the lapping quality and efficiency.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".