Effects of Feedstock Mixing Composition on the Cold Sprayability of Bimodal Size 316L/Fe Powder Mixtures
Bibliographic record
Abstract
Abstract In this study, 43 μm 316L stainless steel and 23 μm commercial purity Fe feedstocks were used. The following coatings were made by cold spray: single component 316L, Fe, and their binary composites with nominal compositions of 20 wt.% Fe (20Fe), 50 wt.% Fe (50Fe) and 80 wt.% Fe (80Fe). The coatings were characterized (microstructure, flattening ratio, composition) and the cold sprayability metrics (DE, porosity, coating cohesion strength) were analyzed. Results show that the single component 316L coating has a much better DE and coating cohesion strength, and a slightly lower porosity as compared with the Fe coating, whereas all the composite coatings have the similar cohesion strength. Moreover, the 20Fe coating features the highest porosity and the lowest DE; 50Fe coating features the lowest porosity; and the 80Fe coating features the highest DE. To characterize the feedstock mixture composition, in addition to the usual approach of weight or volume fraction, the ratio of the 316L and Fe particle numbers in a mixture (i.e. particle number fraction), was calculated. Using this metric, the effects of the feedstock mixing composition on the cold sprayability of bimodal size 316L/Fe powder mixtures can be better explained.
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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) | 0.000 | 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".