Numerical Simulation of Water Quenching of Large Size Steel Forgings: Effects of Macrosegregation and Grain Size on Phase Distribution
Bibliographic record
Abstract
In this paper, water quenching of large ingots was simulated using FORGE NxT 1.1® Finite Element code. Simulations were carried out for as-forged medium-carbon low-alloy steel. A novel method is proposed to simulate the different parts of a large size forged block with different chemical compositions and grain sizes using the multiple materials method. The effects of macrosegregation, grain size variation and cooling rate on phase distribution through the volume of the forged block were investigated. The delay in transformation kinetics, which is due to the effect of grain size variation and carbon content, was analyzed. Results show that macrosegregation and grain size variations significantly influence transformation start points and the volume fraction of phases that are present in each location of the forged ingot. The proposed prediction method was validated using high-resolution dilatometry experiments and X-ray diffraction measurements to evaluate accurately the volume fraction of martensite, bainite and the percentage of retained austenite for each condition.
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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 | 0.000 | 0.000 |
| 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) | 0.001 | 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".