Characterization and deposition behaviors of cold sprayed mixed 316L/Fe coatings
Bibliographic record
Abstract
The focus of this thesis is to explore the effects of powder characteristics on the cold sprayability of mixed 316L and Fe powders. X-ray diffraction (XRD), electron backscatter diffraction (EBSD) were performed to characterize the feedstock powders before and after mixing. In addition to deposition efficiency (DE), porosity, microhardness and bond strength of the coatings were measured and used as metrics for cold sprayability. Experiments were also performed with a dual powder feeder, which therefore eliminated the need for premixing powders, and introduced new factors which added to the understanding of cold spraying of mixed powder feedstocks. Individual particle impact tests were performed to study the different cold spray behaviors of different particle-substrate combinations, e.g. Fe on 316L, Fe on Fe, etc., since these events could take place in mixed powders cold spray.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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".