Computer Simulation of Microstructure Evolution in Low Carbon Sheet Steels
Bibliographic record
Abstract
Models of microstructure evolution in steels are reviewed. The emphasis of the review is on low carbon sheet steels both hot-rolled and cold-rolled and annealed. First the state-of-the-art on industrial microstructure process models is presented. The individual model concepts for grain growth, recrystallization, precipitation and phase transformations are briefly discussed. The development from empirically-based models to physically-based models is identified as a key issue to have increased predictive capabilities for these models over a wider range of steel grades and operational conditions. The challenges in the development of the next generation of models are delineated. In particular, new aspects of microstructure evolution associated with novel processing routes and advanced high strength steels are evaluated. Further, the majority of the currently employed models are on the macro-scale but future microstructure models will increasingly be meso-scale models that predict actual microstructures rather than a number of average parameters (e.g. grain size, fraction transformed) to describe microstructure evolution.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".