Effect of Surface Oxidation on Transition Temperature of Stainless Steel Substrate Upon Impact of Aluminum Particles
Bibliographic record
Abstract
Abstract Experimental studies involving aluminum particles sprayed onto polished AISI304L substrates using the Valuarc 200 wire arc previously showed that there exists a transition temperature from splash to disk splats. Increasing the substrate temperature above the transition temperature was seen to increase the number of disc splats, thus producing coatings of improved properties. XPS test results have shown that increasing the substrate temperature also results in increased oxygen content on the surface of the substrate. Experiments also show that prolonged heating of a substrate at a particular (fixed) temperature further promotes oxidation of the substrate surface, thus increasing the surface roughness (Ra). Samples generated on substrates that were held at or above 350°C (above Tt) for prolonged periods of time (over 20 minutes) were seen to promote splashing. This is in contrast to the previous findings that showed substrate temperatures above Tt further promoted disc type splats and improved adhesion between the splat and substrate. Samples generated in this study consistently showed that splashing can be seen at temperature well above the transition temperature, if the substrate has been heated for too long a duration. The cause of splashing is believed to be related to increased surface roughness resulting from prolonged oxidation of the substrate surface. Abstract only; no full-text paper available.
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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 | 0.000 |
| 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) | 0.002 | 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".