Effect of Parameters to the Coating Formation during Cold Spray Process
Bibliographic record
Abstract
In this study, the effects of parameters such as substrate hardness, substrate surface roughness and stand-off distance to the coatings formation and properties of the coatings during cold spraying process were examined. For this purpose, coatings were deposited onto the copper substrate with using of commercially available copper powder. Substrate hardness of 55 HV, 107 HV and 140 HV were obtained by heat treatment and deformation hardening methods. Different substrate surface roughness was obtained by using of SiC emery papers which have different grits and stand-off distance during cold spray process was manipulated as 5, 10, 20 and 30 mm. After the production of the coatings, characterisation procedures were performed by microstructural observations which are optical and scanning electron microscope surveys, porosity concentrations, coating thickness and hardness measurement.
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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 | 0.000 | 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) | 0.000 | 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".