Study of the Interfacial Dynamic Behavior During Slat Formation Alumina on Steel Substrate by FSI/VOF
Bibliographic record
Abstract
Thermal spraying involves surface treatment technologies in which a finely divided material is sprayed at high velocity and in a molten or semi-molten state onto the part to be covered. Their main application is the protection against wear, corrosion, and thermal effects. They also have functional properties (electrical, magnetic, etc.), which make them suitable for various industrial uses. The success and shelf life of plasma deposits depends to a large extent on the quality of the adhesion between the deposit and the substrate or between the lamella that constitute the deposit and which are generated by the impact of the powder particles crushing on the substrate. In this work, the spreading and solidification of an alumina particle on a substrate of the stainless-steel under the plasma projection conditions are investigated. The study is conducted by using the interaction fluid-structure (FSI) method in the ANSYS-code. This method is selected to determine the mechanical characteristics at the interface of the substrate particles during rolling. A focus is made on the increased role of the deformations developed during spreading at the surface interface of the substrate, the concentration of the stresses on the adhesion of the lamella, and consequently the coating quality.
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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".