Modelling of microstructure evolution in advanced high strength steels
Bibliographic record
Abstract
There is currently a significant development of new families of steels, i.e. advanced high strength steels, in response to the demands of the automotive and construction industries for materials with improved property characteristics.The austenite-ferrite transformation is the key metallurgical tool to tailor the properties of steels.The design of processing paths that will lead to the desired microstructures is increasingly been aided by computer simulations.The present paper illustrates state-of-the-art microstructure modelling approaches for low carbon steels considering three important processing aspects: (i) run-out table cooling of hot-rolled steels, (ii) intercritical annealing of cold-rolled sheets, (iii) girth welding of linepipe steels.Phenomenological models based on the Johnson-Mehl-Avrami-Kolmogorov (JMAK) approach incorporating additivity are now available to describe phase transformations during run-out table cooling of microalloyed steels.Strengths and limitations of this approach will be discussed.Process models for intercritical annealing require an accurate description of the austenite formation kinetics where morphological complexities can be captured using the phase field approach.During girth welding the control of the microstructure in the heat affected zone (HAZ) is of paramount importance.The HAZ experiences rapid thermal cycles and steep temperature gradients.Phase field modelling is an excellent tool to describe the role of these spatial constraints as will be illustrated for austenite grain growth.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".