Plasma-Texturing Surface Treatment of Grey Cast Iron for Friction Reduction
Bibliographic record
Abstract
A new surface texturing technique, based on liquid plasma discharging in an aqueous electrolyte, is proposed to modify the surface morphology of grey cast iron. During the process, a grey cast iron sample serves as a cathode where the reduction of hydrogen from the aqueous electrolyte occurs and consequently plasma discharging is generated on the sample surface under applied high voltages (up to 480V). The formed hydrogen bubbles are exploded during the electrical discharging, leaving an irregular array of craters on the sample surface due to the high temperature and shockwaves of the plasma micro-arc discharging. After polishing the crater-like textured surface, surface roughness and oil retention are measured by a profilometer. Reciprocating tribotests are utilized to determine the coefficients of friction. The surface morphology of the polished and tested surface is studied by SEM. The same tests are also conducted for the cast iron with a cross-hatched surface. These two set of results are compared to determine the effects of the texturing and polishing on friction. The results show that the polishing of textured surface can decrease the roughness and coefficients of friction significantly at starved lubricating conditions. This method has potential to be applied on the cylinder bore surface of a cast iron liner for the internal combustion engine (ICE). By honing the textured bore surface, the friction between piston and cylinder bore is expected to decrease and the ICE efficiency to increase for environmental benefits.
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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 | 0.000 |
| 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) | 0.001 | 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".