Bibliographic record
Abstract
Background: Various relevant patents and papers which have reported noncircular gears synthesize the advantages of circular gears and cam mechanisms, and are widely used in many types of mechanical instruments. Hobbing is a better method for fabricating noncircular gears. There are 4 linkagemethods to hob noncircular gears. However, which linkage method should be chosen practically has not yet been reported. Objective: The goal of this work is to choose the best linkage method for hobbing noncircular gears. Method: Firstly, the hobbing models of noncircular gears was deduced. Then, based on the model, hobbing linkage methods of noncircular gears were obtained. Thirdly, under different hobbing linkage methods, their aspects (developing regularity of hobbing cutter trace, arc length of program blocks and motion axes of machine tools) were compared. Results: Finally, with the best characteristics of a high density of shaping cutter trace, high uniformity of arc length of program blocks and ease of control, the equal arc-length of gear billet (EALGB) is obtained. It has been proven that EALGB is an excellent linkage method to hob noncircular gears. Conclusion: It has been proven that EALGB is an excellent linkage method to hob noncircular gears.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".