The effect of oxidation on tribology behavior of nickel-graphite coated stainless steel SS420
Bibliographic record
Abstract
Abstract The main solution to the challenge of maintaining maximum sealing in the compressor section of each gas turbine is the use of abradable coatings. These coatings have a double duty, including (a) maintaining the lagging and (b) protecting the tips of the rotor blades. Choosing the type of abradable coating primarily depends on the service temperature of the coating. Nickel-graphite (Ni-G) coating is a good choice for use up to 480 °C and, or steel/sub-alloy rotor blades. In this research, the Ni-G coating was applied by the flame spraying method of Ni-G powder with a thickness of about 250 μm on an SS420 stainless steel substrate. The effect of the composition of the bonding layer was also investigated using two compositions, Ni-5Al and NiCrAlY. Obtaining the knowledge of applying Ni-G coating by flame spraying, identifying the structural and compositional characteristics of the coating (through optical and electron metallography), and the effect that oxidation can have on the tribological behavior of the coating were among the goals of this project. The best conditions for spraying the Ni-G coating were achieved an oxygen gas pressure of 6 bar, oxygen flow rate of 18 L min−1, acetylene pressure of 1.5 bar, acetylene flow rate of 24 L min−1, and the distance between the gun head and the sample surface was 22 cm. The results showed that placing the coating in oxidizing conditions increases its coefficient of friction. The increase in the coefficient of friction was attributed to the formation of oxide shells on the surface of the coating after 500 h of exposure to oxidation conditions. Corresponding to the higher coefficient of friction, the oxidized coating showed a decrease in wear resistance as a result of oxidation. This result can show the decrease in abradable of this coating with increasing service time.
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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".