Characterization of Hot Deformation behavior for Ultra-High Strength (UHS) Steel containing Tungsten
Bibliographic record
Abstract
The steel alloy of the current study contains 0.3% carbon with different amounts of Cr, and Mo, in addition to W. Single simulation hits were designed as isothermal passes for compressive hot deformation up to 0.5 true strain. Each simulation compressive pass was carried out at temperatures, 1050, 950, 850 and 750 oC, with strain rates 0.1 and 1.0 sec-1. A numerical computational model is used for formulation of the alloy hot flow behavior at the specified conditions. The model considers inseparable strain hardening mechanism and dynamic softening phenomenon. It is noticed that high deformation temperature lowers the flow stress value (σf), while the increase of the strain rate raises it. The flow curves reveal initial high strain hardening phenomena in combination with low dynamic softening features. With the increase of strain, the dynamic softening, becomes dominant, and the flow stress gradually drops until a relatively stable stress level. Predicted flow curves were then constructed and compared with their counterpart experimental flow curves. The predicted flow curves are typically matching the counterpart experimental ones.
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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".