The Effects of Optimized Nozzle-Substrate Distance on Cold Gas Dynamic Spray (CGDS) Process
Bibliographic record
Abstract
This paper presents research that is focused on the particle coating quality of Cold Gas Dynamic Spray (CGDS) process. The coating quality is affected by several different factors, namely the carrier gas species, nozzle-substrate distance, nozzle inlet pressure and the coating particle size. The intent of this research is to use numerical simulations to predict the coating quality and to find the optimized nozzle-substrate distance and particle size in CGDS process by tuning the factor of nozzle-substrate distance and the coating particle size. Air was chosen as the carrier gas to accelerate copper (Cu) particles, which have diameters ranging from 2–50μm.. There are two main target factors, the nozzle-substrate distance and particle size, which are going to affect the coating quality in this study. In the first part, 14 sets of nozzle-substrate distance models ranging from 2.5mm to 100 mm were setup to study the air velocity, density, temperature and pressure contour through the De-Laval nozzle to the aluminum substrate. In the second part, 49 sets of different particle sizes ranging from 2–50μm in diameter were computed. The particle’s impact velocities on the aluminum substrate were applied to 12 different nozzle-substrate distance models. The bow shock wave, a high pressure gradient region, formed in front of the aluminum substrate, makes the copper particles decelerate in front of the substrate. The results showed that the gas flow velocity contours was affected by different nozzle-substrate distances, which caused different particle accelerating characteristics.
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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".