Formation and evolution of craters in carbon steels during low-energy high-current pulsed electron-beam treatment
Bibliographic record
Abstract
The authors investigated in detail the formation and evolution of microcraters induced by low-energy high-current pulsed electron-beam treatment on several quenched and tempered carbon steels. They have shown that the crater formation mechanism is the same for the three selected steels regardless of the carbon content and original microstructure state. Melting starts at the subsurface layer during treatment, resulting in the nucleation of small droplets preferentially at grain or phase boundaries. Under further heating, the boiling droplets erupt through the surface. The liquid around these craters shrinks to supply the lost part and, during the cooling process, leads to the formation of the funnel-like crater morphology. Microirregularities help retain locally the heat flux and, consequently, serve as nucleation sites for crater formations. By increasing the number of pulses, microirregularities were gradually removed and melted layer depth increased. As a result, crater formation became less effective. On the other hand, some of the already formed craters were deepened, while others were eliminated during the following pulses. The above processes together cause the crater density to first increase and then decrease, whereas the surface roughness first increases and then remains at the same level with increasing number of pulses.
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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) | 0.001 | 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".