An Economical Approach to Cold Gas Dynamic Spraying Using In-Line Nitrogen- Helium Blending
Bibliographic record
Abstract
Abstract The cold gas dynamic spraying process solves many issues with respect to the deposition and additive manufacturing of metals. Namely, it provides a reduced reactive environment, simplicity of operation, and high deposition rates. It is known that the deposition efficiency of the cold spray process can be substantially increased using helium instead of nitrogen as the process gas. However, the use of pure helium can be cost prohibitive in many situations and commercially available helium recovery systems constitute a major capital investment on top of the spray system and ancillary equipment. This work focuses on the development and use of a novel, inline gas mixing system, designed to provide a blend of nitrogen and helium at any ratio. Deposits produced with different gas ratios were investigated through particle velocity, deposition efficiency, porosity, and hardness. The experimental results show that helium, even in lower percentages, can have a significant effect on deposition efficiency and that helium percentage can be optimized to reduce the overall coating production costs. From the results, a cost model is presented which, when provided experimental values and user costs, can be used to identify the nitrogen-helium ratio that will produce the lowest overall coating cost.
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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 | 0.000 |
| 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) | 0.001 | 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".