Behavior and Characterization of Two 7-8 wt% Yttria-Stabilized Zirconia Powders and Coatings Produced Using Plasma Spray Deposition
Bibliographic record
Abstract
Abstract Two 7-8 wt% yttria-stabilized zirconia powders of similar size and chemistry but having different microstructure properties and manufacturing routes were studied. One significant difference was the density and internal porosity of the starting powders. Deposition efficiency (DE) of the low density (LD) powder was found to be higher and less sensitive to changes in the spray process parameters than the high density (HD) powder. Probing the in-flight particle characteristics with the DPV- 2000 made it possible to link the observed DE values with the in-flight particle temperature. For each powder, DE was found to depend mainly on a single variable, the in-flight particle temperature. DE was found to vary strongly with particle temperature for temperatures under 2700°C, whereas the dependence with particle temperature was much less important above 2700°C. Variations in DE seemed to evolve according to variations of the melted fraction of the sprayed material. Since the LD powder was found to achieve higher particle temperatures at given spray conditions, DE was found to be higher for the LD material and the range of variations in DE was found to be much less than that observed with the HD material. Examination of the coating microstructures revealed that a coating produced with the LD powder had slightly higher porosity than that produced with the HD powder at similar inflight parameters. Spraying at higher in-flight particle temperature or velocity, which resulted in higher and more robust DE values, tended to yield coatings with lower porosity, resulting in coating density exceeding the tolerance range specified by some end-users. Increasing the powder feed rate and using conditions that produced a higher in-flight particle temperature were found to increase the porosity up to an acceptable level without significantly degrading DE. Therefore, a solution was found that not only reduced the sensitivity of DE to changes in the spraying conditions but also increased the rate of production by reducing the time required to spray the part.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".