Effects of Gear Oil Properties on Pitting Life in Rolling Four-Ball Test Configuration
Bibliographic record
Abstract
There is a connection between the efficiency of oils and their wear and/or surface damage protective properties, an area not so well described in the literature. One such damage mode is macroscale contact fatigue on gear tooth flank surfaces, also called pitting. The present study is aimed at investigating the correlation between gear oils’ physical properties, important in terms of gear transmission losses, and pitting life. Eight gear oils were formulated giving different combinations of base oil, viscosity, and concentration of friction modifiers. All eight oils also contained an additive package designed to meet GL-5 specifications. This study consists of three parts. In the first, the oils’ physical properties were measured using a set of bench tests. In the second, the pitting lives of the oils were evaluated using rolling four-ball tests. The third part deals with the correlation between the measured physical properties of the oils and their pitting lives. This is achieved through multiple linear regression, with a view to finding the salient properties that have a significant influence on pitting life. The results show that gear oils’ physical properties do have a large influence on the pitting lives. Oil properties that lower interfacial tangential stresses are beneficial in enhancing pitting life.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".