On Lubrication States after a Running-In Process in Aqueous Lubrication
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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 (Ef) 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, Ef, 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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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) | 0.000 | 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 it