A Novel Rolling-Annealing Cycle for Enhanced Deep Drawing Properties in IF Steels
Bibliographic record
Abstract
To give good drawability, a steel needs high volume fractions of the annealing texture component {111} and a low fraction of ∼{100}<011>. This is achieved in conventional Interstitial Free (IF) steels by a cold rolling reduction of 85% and an anneal at 750°C-800°C for a few minutes. In this research, a double rolling and annealing process is examined based upon the notion that if {111} can be produced, further rolling of the material should provide nuclei of {111} by the process of deformation banding. In Canadian prize winning work it was demonstrated that rolling ferrite at 700°C, produced a strong {111} texture after annealing at 700°C and so this was also selected for further rolling and annealing. The results were highly encouraging, the intensity of {111} increased to levels well above 30X Random. An Orientation Imaging Microscopy (OIM) investigation revealed that the {111} oriented grains were subject to orientation splitting around <111>ND, and this process of deformation banding produced the necessary lattice curvature for nucleation of the texture components essential for good deep drawability. A detailed investigation of such two stage deformation processes was undertaken in which the total strain was kept constant, with first and second rolling interrupted by annealing before the final recrystallization anneal was made. The results are complex, but it is certain that <111>{hkl} as a starting orientation before second rolling is essential for the success of the process.
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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 | 0.000 | 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".