Study of the characteristics of plasma spray sealing aluminum silicon-polyester coatings
Bibliographic record
Abstract
This study shows the homologation of the plasma spray parameters of soft abrasive AlSi - Polyester seals so that they can be applied on the TV2 - 117A compressor engines. The research has aimed at substituting existing sealants with a new class of materials in order to increase the sealing effect under the highest levels of pressure and to provide the air flow temperature of 100-125°C through the compressor. The Metco 601NS material and plasma spray technology were applied on the air labyrinth ring as a part of the TV2-117A turbojet engine compressor in order to obtain soft sealing. The deposit parameters were carefully selected in order to obtain coatings with the best characteristics depending on their application.The flow of helium was taken as a basic parameter in the parameter selection procedure. The coating with the best mechanical and structural properties was deposited on the air labyrinth ring to examine the effect of the coating application in an assembly. The microstructures of deposited layers were estimated with a light microscope and a (SEM) Scanning Electron Microscope. The microstructural analysis of deposited layers was performed according to the Pratt - Whitney standard. The assessment of the mechanical properties of the coatings was done by examining the macrohardness of the sealing layers with the HR15Y method. The coating bond strength was tested by tensile testing. The effect of the air labyrinth ring sealing was tested inside the TV2-117A engine compressor on the test station for a period of 42 hour.
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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 | 0.001 |
| 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".