Experimental Analysis of Friction and Wear of Self-Lubricating Composites Used for Dry Lubrication of Ball Bearing for Space Applications
Bibliographic record
Abstract
Lubricating space mechanisms are a challenge. Lubrication must be sustained in different environments, for a very long period of time, and without any maintenance possible. This study focuses on the self-lubricating composite used in the double transfer lubrication of ball bearing. Ball/races contacts are lubricated via the transfer of materials from the cage that is made of the composite. A dedicated tribometer has been designed for the study. A specificity of the tribometer is to not fully constrain the composite sample but to let it move, as the cage would do in the bearing. Four composites (PTFE, MoS2, glass or mineral fibers) where tested in ultrahigh vacuum and humid air environments. Transfer was achieved with morphologies and composition similar to what is observed on real bearings. Adhesion measurements performed on composite materials before and after friction allowed one to explain the differences in tribological behaviors observed (transfer quality and contact instabilities). Beyond strengthening the composites, fibers are shown to be critical in trapping mechanically and chemically the transferred material to lubricate and prevent instabilities. Equilibrium between internal cohesion of transferred material, and adhesion to counterparts must be satisfied. Mass spectrometry showed that water appears also critical in the establishment of stable transfer film, even in vacuum.
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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 | 0.001 | 0.001 |
| 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) | 0.001 | 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".