On Lubrication States after a Running-In Process in Aqueous Lubrication
Bibliographic record
Abstract
Recently, many studies have reported the ultralow friction coefficient of sliding friction between rigid solid surfaces in aqueous lubrication. A running-in process that goes through high-friction and friction-decreasing regions to a stable ultralow friction region is often required. However, the role of the friction-decreasing region is often ascribed to tribofilm formation in which complexity hindered the quantitative description of the running-in process and the prediction of its subsequent lubrication state. In this work, the frictional energy ( E f ) dissipated in the running-in process of a poly(oligo(ethylene glycol) methyl ether acrylate) aqueous lubrication was related to the wear of solid surfaces under different conditions and lubrication states. Experimental results indicated that the high-friction region was in a boundary lubrication state, contributed to most of the wear, and significantly reduced the contact pressure, whereas the friction-decreasing region was in a mixed lubrication state, contributed only to the slight and slow removal of materials, and slightly reduced the contact pressure. Therefore, by establishing relationships among the wear scar diameter, E f, and the Stribeck curve of the tribological system, the subsequent lubrication state after a running-in process under various working loads and sliding speeds could be quantitatively predicted. The running-in experiments with different aqueous lubrication systems showed good agreement with the prediction of this method. This investigation provides an effective method for the wear and lubrication state prediction after a running-in process, further proving the importance of the Stribeck curve for a lubrication system. This study may also have important implications for the strategy design of the running-in process in various industrial applications.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".