Influence of HVAF Process Parameters on In-Flight NiCoCrAlY Particle Oxidation and Corresponding Splat Characteristics
Bibliographic record
Abstract
Abstract The deposition of MCrAlX coatings (where M is Ni, Co, Fe, or a combination of these, and X is Y, Si, Ta, Hf, or a combination of these) via thermal spraying has acquired significant importance in industries such as aerospace, power plants, oil, and gas, etc. Among various thermal spray deposition techniques, high-velocity air fuel (HVAF) has shown a growing potential for the deposition of metallic powders which are sensitive to high-temperature oxidation during spraying. Thus, it is essential to understand the in-flight behavior of these metallic particles in the high-velocity, low-temperature HVAF flame. In this work, a NiCoCrAlY powder was sprayed using two sets of HVAF deposition parameters onto stainless steel substrates. In-flight particle diagnostic tools such as AccuraSpray were employed to understand the behavior of these spray particles. The deposited particles were comprised of partially molten particles and fully deformed splats. Samples with higher powder feed rates showed a primary coating buildup on the substrate surface. EDS plots revealed no traces of inflight particle oxidation but contained carbon residue due to the presence of unburnt hydrocarbons from the fuel-rich HVAF-M3 torch. This study provides a preliminary understanding towards the significance of deposition parameters on the in-flight particle oxidation behavior and splat deformation characteristics by HVAF spraying.
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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".