Predicting the Occurrence of Dynamic Transformation and Rolling Mill Loads Drops by Hot Torsion and Compression Testing
Bibliographic record
Abstract
Flo w curves were determined in hot compression and hot torsion at a series of temperatures on eight C steels of increasing C concentration. The critical strains for the init iation of dynamic transformat ion (DT) as well as of dynamic recrystallizat ion were determined by the double differentiation method. It is shown that dynamic transformation is init iated well before dynamic recrystallization under industrial conditions of rolling. The mean flow stresses (MFS's) pertaining to each experimental condition were then calculated fro m the flo w curves by integration. These are plotted against inverse absolute temperature in the form of Boratto diagrams. The stress drop temperatures, normally defined as the upper critical temperature applicable to rolling Ar3*, were determined fro m these diagrams. These are shown to be about 40°C above the paraequilibriu m and about 20-30℃ above the orthoequilibriu m upper critical transformation temperatures. This type of behavior is ascribed to the occurrence of dynamic transformation during deformat ion. The general characteristics of the dynamic transformation of austenite to ferrite are reviewed. It is suggested that some o f the unexpectedly low ro lling loads, as well as the load drops that have been reported to take place above the Ae3 temperature in strip mills, may be attributable to this phenomenon.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".