Effect of Substrate Temperature on the Formation Mechanism of Cold Sprayed Aluminum, Zinc, and Tin Coatings
Bibliographic record
Abstract
Abstract When describing the cold spray process, one of the most widely used concepts is the critical velocity. Current models predicting critical velocities take the temperature of the sprayed particles explicitly into account but not the surface temperature (substrate or already deposited layers) on which the particle impact. This surface temperature is expected to play an important role since the deformation process leading to particle bonding and coating formation takes place both on the particle and the substrate side. The aim of this work is to investigate the effect of the substrate temperature on the coating formation process. Experiments were performed using aluminum, zinc and tin powders as coating materials. These materials have a rather large difference in critical velocities that gives the possibility to cover a broad range of deposition velocity to critical velocity ratio using commercial low pressure cold spray system. The sample surface was heated and the temperature was varied from room temperature to a high fraction of the melting point of the coating material for all three materials. The change in temperature of the substrate during the deposition process was measured by means of a high speed IR camera. The coating formation was investigated as a function of (1) the measured surface temperature of the substrate during deposition, (2) the gun transverse speed and (3) the particle velocity. Both single particle impact samples and thick coatings were produced and characterized. Both the particle-substrate and interparticle bondings were evaluated by SEM and confocal microscopy
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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".