A Study on the Powder Manufacturing Effect on Flowability and Deformability of Pure Cu Powders in Cold Spray Application
Bibliographic record
Abstract
Abstract The quality of feedstock powder is a critical factor in determining the properties of coatings deposited using cold spray (CS). However, most commercially available powders are not designed or optimized for CS applications, making it challenging to tailor powders to desired quality. In this research, we investigated and compared the cold-sprayability of four different Cu powders produced by electrolysis and gasatomization methods. We assessed the powders' microstructure, particle morphology and size distribution to understand the effect of manufacturing methods on Cu powder characteristics. We also studied the flowability of the powders using the shear cell method and evaluated their mechanical properties and deformability for CS using nano-indentation. Our results showed that gas-atomized powders with equiaxed grains exhibited promising flowability and deformability for CS applications, outperforming the other powders tested. Specifically, the spherical morphology of gas-atomized powders provided less surface area than the irregular-shaped electrolytic powder, reducing the interaction of surface forces and contributing to smooth powder flow. Additionally, the gasatomized powder with small dendrites in the microstructure exhibited the highest nano-hardness value (HIT= 1.6±0.1 GPa), while the porous electrolytic Cu powder had the lowest value (HIT= 0.7±0.2 GPa). In conclusion, we found that gas-atomized Cu powders with equiaxed grains may hold promise as the optimised feedstock for CS application, considering both effective metrics of flowability and deformability.
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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) | 0.000 | 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".