Processing and Properties of Titanium Coating Produced by Warm Spraying
Bibliographic record
Abstract
Abstract Thermal spraying of dense titanium coatings in the air atmosphere was achieved by using a two-stage high velocity oxy-fuel process (HVOF) called the Warm Spray Process. In the process nitrogen gas is mixed with the combustion gas to lower the gas temperature. Gas dynamics modeling of the flow field of the gas in the spray apparatus as well as the acceleration and heating of titanium powder injected from the powder feed ports were conducted. Based on the obtained temperature history of a titanium powder particle, its oxidation during flight was also predicted by using a Wagner-type oxidation model. These results were compared with measured velocity and temperature of sprayed particles by DPV2000 and the properties of deposited coatings. Significant discrepancy in the temperature of sprayed particles was found between the calculation and measurement whereas the measured velocity was closer to the model calculation. The model prediction of oxygen content was in a good agreement with the analysis of actual coatings. Furthermore, properties of the sprayed coatings such as porosity, oxygen content were correlated with the particle velocities and temperatures. Nitrogen gas was highly effective in lowering the oxygen content, but excessive nitrogen addition caused the coating porosity to increase due to insufficient particle temperatures.
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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".