Coating Formation by Impact of Molten Metal Droplets with Uniform Size and Velocity
Bibliographic record
Abstract
Abstract The adhesion of splats formed by impact of molten metal droplets was studied experimentally. Tin droplets (550 µm diameter) were produced using a drop-on-demand generator. To achieve high impact velocities the stainless steel coupons used as substrates were mounted on the rim of a rotating flywheel and heated using cartridge heaters. To hit a falling droplet with the substrate and photograph its impact, a timing circuit was used to synchronize three events with the position of the substrate: ejection of a droplet, triggering of the camera and a flash to provide illumination. The impact velocity was varied from 10 – 40 m/s whereas the substrate average roughness (2.0 µm) and the droplet diameter (~550 µm) were kept constant. We measured the adhesion strength of splats by a simple pull test. A wire was attached to the upper surface of each splat using epoxy and the force required to separate the splat from the substrate was recorded. A significant increase in adhesion strength was observed as the impact velocity was increased. Coatings were produced by depositing many droplets sequentially. Substrate temperature and impact velocitiy were the main parameters varied. SEM images of cross-sections through coatings showed that increasing impact velocity and substrate temperature produced better adhesion between the coating and substrate.
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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".