Modeling the Formation of Thermal Spray Coatings on Substrates with Arbitrary Shapes
Bibliographic record
Abstract
Abstract The formation of Nickel coatings on stainless steel substrates and YSZ (Yttria-Stabilized Zirconia) on NiCrAlY in the Atmospheric Plasma Spray (APS) process is investigated. Coating formation over a substrate with an arbitrary shape (an inclined step in this paper) is considered. The topography of the coatings, as well as their microstructure, e.g., porosity, average thickness, and average roughness, are evaluated. An algorithm, which is based on the Monte-Carlo stochastic model, is employed. The significant difference between the coating temperature and that of the substrate leads to the formation of residual thermal stresses. These stresses are analyzed using Object Oriented Finite-element software (OOF) developed by the National Institute of Standards and Technology (NIST). An image of the cross-section of the coating is imported into the code, which utilizes an adaptive meshing technique and Finite- Element Method to calculate residual thermal stresses. The maximum stress in the coatings occurs at the interface between the coating and the substrate. The coatings' topography and microstructure are compared with those of the experiments.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".