A comparative review on cold gas dynamic spraying processes and technologies
Bibliographic record
Abstract
Cold gas dynamic spraying (CGDS) is a relatively new technology of cold spraying techniques that uses converging-diverging (De Laval) nozzle at a supersonic velocity to accelerate different solid powders towards a substrate where it plastically deforms on the substrate. This deformation results in adhesion to the surface. Several materials with viable deposition capability have been processed through cold spraying, including metals, ceramics, composite materials, and polymers, thereby creating a wide range of opportunities towards harnessing various properties. CGDS is one of the innovative cold spraying processes with fast-growing scientific interests and industrial applications in the field of aerospace, automotive and biotechnology, over the past years. Cold gas spraying with a wide range of materials offers corrosion protection and results in increases in mechanical durability and wear resistance. It creates components with different thermal and electrical conductivities than that substrates would yield, or produces coatings on the substrate components as thermal insulators and high fatigue-strength coatings, and for clearance control, restoration and repairing, or prostheses with improved wear, and produces components with attractive appearances. This review extensively exploits the latest developments in the experimental analysis of CGDS processes. Cold gas dynamic spraying system, coating formation and deposit development, description of process parameter and principles, are summarized. Industrial applications and prospectives of CGDS in future research are also commented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.003 |
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".