Influence of Flame Parameters on Stainless Steel Coatings Properties
Bibliographic record
Abstract
Abstract Owing to high particle velocity upon impact, and consequently low porosity and high bond strength of so-obtained coatings, HVOF spraying process is widely used to improve components life in service. However, many parameters can affect metallic coatings properties, especially un-melted particles and oxidation level. Flame parameters, such as calorific power, combustion ratio and temperature, are of prime importance. The aim of this work was focused on the influence of these parameters on stainless steel coatings characteristics. For different substrate temperatures, maintained through CO2 cooling nozzles, those parameters varied independently. Flame characteristics were computed using a simple model for propylene as fuel gas. Microstructure investigation as well as oxide content measurements and microhardness were obtained. It appeared that combustion temperature, in the range studied (2600-2750K) was not a critical factor. However, combustion ratio and calorific power greatly influenced coating properties: an increase of oxide content, and consequently a higher microhardness, was observed when combustion ratio decreased as well as when calorific power increased.
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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".