Effect of Spray Parameters on MCrAlY In-Flight Particle Oxidation and Coating Microstructure Deposited by Internal Diameter High Velocity Air Fuel (ID-HVAF)
Bibliographic record
Abstract
Abstract Aircraft gas turbine blades operate in aggressive, generally oxidizing, atmospheres. A solution to mitigate the degradation and improve the performance of such components is the deposition of thermal barrier coatings (TBCs). Specifically for bond coats in aerospace applications, High Velocity Air Fuel (HVAF) is very efficient for coating deposition. However, internal diameter (ID) HVAF has received little attention in the literature and could be a promising alternative to limit oxidation during spraying when compared to conventional methods. The main objective of this study is to analyze how the ID-HVAF process influences the microstructure of NiCoCrAlY coatings. To that end, an i7 ID-HVAF torch is used to deposit NiCoCrAlY splats on a steel substrate with different stand-off distances. The deposited splats showed the presence of craters, and both partially melted and deformed particles at the surface. The particle velocity data was recorded, and the splat deformation and amount of particles deposited was shown to be directly corelated to the stand-off distance. The material composition analyzed and quantified by Energy Dispersive Spectroscopy (EDS) did not reveal any traces of in-flight of particle oxidation, but further investigation is required. This study provided a preliminary understanding towards the importance of stand-off distance on the splat deformation and in-flight oxidation.
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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".