Effect of Secondary Component Properties when Cold Spraying Mixed Metal Powders on Carbon Fiber Reinforced Polymers
Bibliographic record
Abstract
Abstract In previous studies at McGill University, tin was successfully cold sprayed onto carbon fiber reinforced polymers (CFRPs). A “crack-filling” mechanism was described as the deposition mechanism that allowed deposition of tin onto the CFRP. Improving the coating conductivity for lightning strike protection (LSP) purposes was achieved by adding other metal powders (aluminum, copper, zinc) to tin and cold spraying on the CFRP. At the same time, it was noticed that the addition of this secondary component (SC) provided an increase in deposition efficiency (DE), tamping was initially hypothesized to explain this improvement, thus prompting a study solely on the effect of SC hardness, which is reported elsewhere in this conference. However, it is recognised that other powder characteristics may also be influencing the DE. Thus, in this study, SCs with a wider variety of particle sizes, morphologies, densities and hardness values were mixed with tin and sprayed on CFRPs. The effect of SC properties on tin deposition is discussed and an optimal combination of SC properties for cold spraying of tin is suggested.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.004 | 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".