Numerical optimization of cold gas dynamic spray process parameters through response surface methodology
Bibliographic record
Abstract
Copper-Zinc and its alloys are extensively used for the corrosion protection of the metal substrate surfaces like steel. Cold gas dynamic spraying (CGDS) is a material coating technique in which metal particles directly adhere to the substrate surface at a relatively low temperature. Numerical simulation (CFD) is a better alternative to understanding CGDS as compared to experimental data due to less expenditure and less energy consumption in CFD. Optimisation of CGDS can be done either by varying the process parameters or by improving the design of the de-Laval nozzle. In this study, the Response Surface Methodology (Central Composite Design) was used to optimise cold spray deposition efficiency by using a 2-D axisymmetric model by varying the process parameters such as carrier gas temperature, stand-off distance, and particle size. Various models were used to predict the critical velocity and erosion velocity. All the process parameters under consideration were found significant in analysing the model by ANOVA. Copper-Zinc Alloy (C93200) particle size is varied from 2.96 µm to 87.04 µm and carrier gas temperature is varied from 287.78K to 708.22K. With the increase in carrier gas temperature and particle size diameter, critical velocity decreases resulting in improved deposition efficiency.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".