Physical and technological features of mechanoactivation of powder particles formed during hydro-vacuum dispersion of metallic melts
Bibliographic record
Abstract
A study has been conducted on the hydro-vacuum dispersion process of metal melts using gray cast iron SCh20 (in Russian nomencluture; 3.3–3.5C, 1.4–2.4Si, 0.7–1Mn, 0.15S, 0.2P in wt %)—an analogue of GG20. It has been revealed that the main factor conditioning the mechanoactivation of formed particles is their solidification in a fibrous non-equilibrium structural-tensioned state. This state is achieved by flattening and asymmetric twistedness of droplets that are detached from the liquid metal in the disperser under volumetric impact of shock-pulsating waves of hydraulic discharge. The degree of particle activation was found to depend exponentially on their dispersion and specific surface area. These parameters determine the degree of asymmetry of shear deformations and the amount of accumulated energy. In turn, the size dispersion and specific surface are significantly influenced by physical and technological factors such as the specific flow rate and pressure of injected water, the thickness and the elevation angle of the hydro shell of the vacuum diffusion funnel, the diameter of the dispersed melt jet passed through it, and its superheating temperature. The control of these parameters makes it possible to smoothly adjust the key ratio “liquid metal: water” and set up the dispersion process with the highest possible degree of size dispersion and activation of the resulting powder.
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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".