Load and Lubricating Oil Effects on Friction of a PEO Coating at Different Sliding Velocities
Bibliographic record
Abstract
<div class="section abstract"><div class="htmlview paragraph">Friction between the piston and cylinder accounts for large amount of the friction losses in an internal combustion (IC) engine. Therefore, any effort to minimize such a friction will also result in higher efficiency, lower fuel consumption and reduced emissions. Plasma electrolytic oxidation (PEO) coating is considered as a hard ceramic coating which can provide a dimpled surface for oil retention to bear the wear and reduce the friction from sliding piston rings. In this work, a high speed pin-on-disc tribometer was used to generate the boundary, mixed and hydrodynamic lubrication regimes. Five different lubricating oils and two different loads were applied to do the tribotests and the COFs of a PEO coating were studied. The results show that the PEO coating indeed had a lower COF in a lower viscosity lubricating oil, and a smaller load was beneficial to form the mixed and hydrodynamic lubricating regimes earlier. The wear scar sizes on counterpart materials were, to some degree, increased with the decrease of oil viscosities at both test loads. It suggests that the benefit of a lower friction (thus, a presumably less wear) induced from a lower viscosity oil couldn’t overweigh a potential concern of wear occurred for a thinner oil. When tested with a very high viscosity of oil, the counterpart material had a significantly less wear, which may indicate that such kind of oil would provide a piston ring with a better protection against the ceramic coating. Thus, a proper selection of lubricating oil for PEO coating would be a decisive factor for balance between reducing friction loss and piston ring wear in an IC engine. The tests were carried out only at a room temperature (~20°C), high temperature tests and floating liner engine dynamometer tests should be done in the future for validation of the benefit of a PEO coating.</div></div>
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How this classification was reachedexpand
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".