An experimental study on the estimation of the static characteristics and relative displacement of the surfaces of an 88% tin-coated plain bearing
Bibliographic record
Abstract
Abstract In order to prevent warming and deterioration in the surface condition of the plain bearing (shaft/bearing), it was necessary to enhance the heat engines' efficiency by applying a coating containing 88% tin to the internal surface of the bearing. To assess the effectiveness and reliability of these coatings, a testing rigs specifically designed for automotive engine bearings was developed. The University of Poitiers collaborated on the experimental analysis, which explored the impact of different loads and speeds. The results of the analysis confirmed that frictional efficiency tended to improve with low loads and high speeds. The displacement observed in the bearing decreased as the speed increased, particularly in lightly loaded plain bearings. Conversely, heavily loaded sliding bearings exhibited higher displacement values. At the 315º position, the displacement was particularly significant and increased significantly with the load, while it was less significant at the 45º position. In the 315º position, the circumferential face displacement of the shaft decreased notably with increasing speed at low loads. Similarly, for higher loads, the displacement only slightly decreased with increasing speed, but these displacements were very high at the 315º position.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".