On the Influence of the Substrate Hardness on Cold-Sprayed Nickel Coating
Bibliographic record
Abstract
Abstract Cold spray process is an emerging technique that produces high density coatings. Particles are carried by a supersonic gas stream through a De Laval nozzle and, finally, impact on a substrate with high kinetic energy. Low gas temperatures make it possible to maintain sprayed material in solid state during the whole process. Beyond a given velocity, called “particle critical velocity”, particles can bind to the surface and create a coating. This velocity is clearly dependent on both sprayed material and substrate properties. This study deals with the investigation of the influence of the substrate hardness on nickel coating properties. Substrates with different hardness but same chemical composition were used. Samples are then coated with pure nickel or NiCrAlY under the same operating conditions. The measurements of some coating properties and process parameters (deposition efficiency (D.E.), level of porosity, micro hardness) allow the analysis of the effect of substrate hardness on sprayability. It was found that only D.E. is widely influenced by hardness of the substrate. It decreases drastically (especially for NiCrAlY) since hardness of the substrate is higher than hardness of the particle. Porosity level and coating hardness remain constant whatever substrate hardness is.
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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".