An Attempt to Improve the Deposition Efficiency of AI2O3 Coating by HVOF Spraying
Bibliographic record
Abstract
Abstract High Velocity Oxy-Fuel (HVOF) method using propylene as a fuel gas was employed to spray alumina particles. In order to improve the coating characteristics such as the deposition efficiency and the hardness, three HVOF gun nozzles of varying geometry were designed and tested experimentally. The spraying process was also simulated numerically for each of the nozzle geometries to understand their effectiveness in influencing the velocity and temperature of the sprayed particles. The coating was characterized using optical and scanning electron microscopy (SEM), micro-vickers hardness test and X-ray diffractometry (XRD). Results showed that with the use of a convergent and divergent type gun nozzle, similar to that of a Laval nozzle, the extent of melting of the alumina particles could be increased. This was exhibited by an increase in the deposition efficiency to the extent of 45%. However, the sharp changes in the convergent and divergent nozzle geometry, resulted in fusion and agglomeration of alumina particles leading to spitting during the spraying process. The results clearly showed that alumina coatings of excellent hardness in the range of 920-1290 HV, with a relatively dense microstructure could be obtained in HVOF method irrespective of the gun nozzle geometry, provided the spraying parameters are properly controlled.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".