Austenite grain growth and hot deformation behavior in a medium carbon low alloy steel
Bibliographic record
Abstract
To examine the austenite grain growth behavior and kinetics under isothermal austenitization process in a low alloy medium carbon forged steel, heat treatments at different temperatures (1150, 1175, 1200, and 1260 °C) and times (5, 15, and 25 min) were conducted. An Arrhenius constitutive relationship was developed to analyze and predict the austenite grain size as a function of the austenitization temperature and time during isothermal austenitization. The model predictions agreed well with the experimental austenite grain size data. Following the austenitization examinations, the hot deformation behavior of the alloy was studied by performing isothermal compression tests for different soaking times (5, 15, and 25 min) at the deformation temperatures of 1150, 1175, and 1200 °C, at a constant strain rate of 0.05 s−1, and up to a true strain of 0.6. The microstructures of the hot compressed samples were assessed to determine the dynamic softening mechanisms and potential austenite grain refinement by dynamic recrystallization (DRX). Under the investigated hot deformation conditions, the flow stress curves and microstructure observations showed DRX characteristics. The flow curves from the peak to the steady-state stress were accurately predicted using a Johnson–Mehl–Avrami–Kolmogorov (JMAK) equation. Based on the flow curves and a mathematical equation, the DRX kinetics were also determined. Variations of the flow curves, DRX kinetics, and dynamic recrystallized (DRXed) grain size with the deformation temperature, strain, and the austenite grain size prior to deformation were analyzed.
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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 | 0.001 | 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".