Diagnostic of the Liquid Injection Behavior in the Case of Axial Suspension Plasma Spray (ASPS)
Bibliographic record
Abstract
Abstract In thermal spraying, controlling particles injection into the plasma plume is crucial and different injection techniques could be used, in particular axial injection. Understanding the impact of axial injection parameters (co-injector gas flow rate and suspension feed rate) is an essential factor in optimizing the coating processes and thus controlling the coating microstructure. Optical (shadowgraphy and particle image velocimetry) and thermal (hot zone length) diagnostics highlighted that the co-injector gas used on the Axial III Plus torch in Suspension Plasma Spraying had no positive effect on suspension atomization and treatment. In the absence of plasma gases, increasing the co-injector gas flow rate significantly improves suspension atomization. However, this benefit is not maintained in the presence of plasma jet because the co-injector gas constricts the suspension in the center of the plasma jet, delaying fragmentation and decreasing particle velocity in the plasma plume. Nevertheless, as the co-injector gas variations are minimal compared to the plasma gas flow, the influence on the microstructure remains relatively low, for example in thermal barrier coating applications. It is recommended to use the lowest possible co-injector gas flow rate to minimize its effect on the kinetic treatment of the particles.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".