Grain Refinement in Hot Rolled Dual Phase Steels
Bibliographic record
Abstract
Currently, grain refinement is being discussed for steels and other materials to increase both strength and toughness. However, for the automotive industry, a good combination of strength and ductility is desired which, for example, dual phase (DP) steels provide. Thus, in the present work the role of ferrite grain refinement is investigated in dual-phase steels. Deformation Induced Ferrite Transformation (DIFT) technique has been applied to produce ferrite grain refinement in four low carbon steels where starting from a conventional DP 600 chemistry Nb and Mo additions were varied. In this thermomechanical processing technique, the steels have been rapidly cooled from an austenitization temperature to the deformation temperature (which is at least 25°C above the Ar3 temperature), to produce highly undercooled austenite, followed by heavy deformation, and subsequently rapid cooling thereby facilitating transformation to fine grained ferrite with martensite and/ or bainite. The effects of austenitization temperature, deformation temperature, and amount of deformation and steel chemistry on the final microstructure of the steels have been studied with tests performed on a Gleeble 3500 thermomechanical simulator. For all investigated steels, the maximum ferrite grain refinement (ferrite grains with a mean grain size of 1-2 μm) has been observed at the highest amount of deformation employed with a true strain of 0.6 for austenitization temperature of 950°C. Comparing hardness values for DIFT-DP microstructures with those obtained from conventional coarse grain DP structures, a strength increase of 20-40% is projected.
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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 | 0.000 | 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".