Tribology and Hot Corrosion Behavior of MCrAlY-Based Multicomponent Coatings Containing Copper
Bibliographic record
Abstract
The use of composite coatings containing solid lubricants is widely reported in the literature, in particular thermally sprayed coatings containing silver. However, these coatings are often limited in their maximum operating temperature due to the melting point of silver and due to reactions between the components at temperatures above 500 °C. In this study, a novel coating is proposed, which consists of an MCrAlY-based matrix and the addition of components (Cu, Mo, and BaF2) to improve the wear resistance at elevated temperatures. The coatings were sprayed by high-velocity oxy-fuel, heat-treated at 1040 °C, and tribologically tested at room and elevated temperatures. Raman spectroscopy and scanning electron microscopy were used on worn and unworn regions of the coating to characterize the changes in microstructure caused by wear. The coatings were also exposed to oxidation and hot corrosion conditions to evaluate the resistance to high-temperature environments. The results have shown an improvement in wear rates of the coatings upon heat treatment and the formation of a smooth tribolayer at 300 °C. The as-sprayed coating was able to withstand the attack by molten salts without exposing the substrate, and minor weight gain was observed, indicating that the MCrAlY matrix was effective to protect the coating and the substrate against damages induced by salt penetration.
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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".