An Economical Approach to Cold Gas Dynamic Spraying Using In-Line Nitrogen- Helium Blending
Why this work is in the frame
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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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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 it