Modeling of Precipitation Hardening during Coiling of Nb–Mo Steels
Bibliographic record
Abstract
Nb–Mo low-alloyed steels are promising advanced high strength steels (AHSS) because of the highly dislocated bainitic ferrite microstructure conferring an excellent combination of strength and toughness. In this study, the potential of precipitation strengthening during coiling for hot-strip Nb–Mo-bearing low-carbon steels has been investigated using hot-torsion and aging tests to simulate the hot-rolling process including coiling. The obtained microstructures were characterized using electron backscatter diffraction (EBSD), highlighting the effects of Nb and Mo additions on formation and tempering of the bainitic ferrite microstructures. Further, the evolution of nanometer-sized precipitates was quantified with high-resolution transmission electron microscopy (HR-TEM). The resulting age hardening kinetics have been modelled by combining a phenomenological precipitation strengthening model with a tempering model. Analysis of the model suggests a narrower coiling temperature window to maximize the precipitation strengthening potential in bainite/ferrite high strength low-alloyed (HSLA) steels than that for conventional HSLA steels with polygonal ferrite/pearlite microstructures.
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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 | 0.000 | 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".