Evaluation of the Influence of Flame Spraying Parameters on Microstructure and Electrical Conductivity of Al-12Si Coatings Deposited on Polyurethane Substrates
Bibliographic record
Abstract
Abstract The influence of flame spraying parameters on coating microstructure and electrical conductivity of aluminum- 12silicon coatings deposited on polyurethane substrates was studied. In order to evaluate the effect of the spray parameters on temperature distribution and its corresponding effect on coating characteristics, an analytical model based on a Green’s function approach was employed. It was found that the addition of air to the flame decreased the temperature within the substrate. Dynamic mechanical analysis (DMA) of the PU substrate revealed that the PU softened as the temperature increased. Therefore, by increasing the pressure of the air injected into the flame spray torch from 35 kPa to 69 kPa, the particles impacted a stiffer substrate. This led to increased deformation of the particles into splats upon impact, improved interlocking, and the overall coating had lower porosity and lower electrical resistance. The results obtained indicated that coating properties are sensitive to both thermal spraying parameters and temperature distribution within the substrate when depositing on elastomeric materials. The effect of torch stand-off distance on coating properties was also evaluated. It was found that higher air pressure can cool the substrate and, therefore, allow for a decrease of the stand-off distance. As a result of shorter stand-off distances, a coating with lower porosity and electrical resistance was deposited.
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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 | 0.000 | 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".