Bibliographic record
Abstract
Search for an improved gear tooth flank shape arose from heavy industry problems rolling mills. Original involute gears suffered severe flank damages. So, better gear teeth flanks should improve contact circumstances, decrease the flank pressure, and enhance a lubrication film. This was achieved by a curved, pole symmetric path of contact by purely graphical methods. And the developed gears, proven in heavy industry applications, showed highly improved properties. Specimens of both gear geometries, which were made of tempered and nitrided alloy steel, were tested on an FZG testing machine, and results confirmed the theoretical foundations of S-gears. Then it was necessary to replace the graphical method by a numerical one and to define the tool. So, the rack profile was defined by a pole symmetric parabolic-type function, which in turn defined the path of contact and finally gears with an arbitrary number of teeth. Many applications were developed with S-gear shape, e.g., helical, crossed, and planetary gears, various worm drives, etc. S-gear concept was also used with polymer gears and high transmission ratio planetary gears. Lately, this concept was used to develop crossed helical gear drive with perpendicular shafts. Such drives are often used in centrifuge drives (e. g. Alfa-Laval) and this implementation with the module m = 5 mm uses a large driving gear with 60 teeth (with the left-handed helix angle of 30°) on the horizontal shaft and a smaller driven gear with 20 teeth (with the right-handed helix angle of 60°) on the vertical shaft. This paper is a tribute to work of Professor Jože Hlebanja (1926-2022) whose research was dedicated to gears with improved properties, namely S-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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".