Effect of Chromium, Boron and Manganese Additions on the Deformation and Recrystallization Textures of Warm Rolled Low Carbon Steels
Bibliographic record
Abstract
The effect of solute carbon content, as well as of chromium, boron and manganese addition, on the warm rolling behavior was investigated.Both the as-rolled and recrystallized microstructures and textures were assessed after rolling at temperatures between 440 and 780°C.In an unalloyed low carbon (LC) steel, intense in-grain shear bands were formed at low rolling temperatures, but this intensity was drastically reduced at higher temperatures.Alloying with chromium and boron significantly enhanced the development of shear bands at the higher rolling temperatures.The intensities of the deformation textures produced were little changed with rolling temperature in the IF steel, but increased markedly with temperature for the LC grade.Conversely, the strength of the LC steel recrystallization texture decreased with increasing temperature.The addition of chromium to the low manganese steel somewhat strengthened the {111} component of the annealing texture at the higher rolling temperatures.However, boron addition resulted in a retained rolling component and severely disrupted the recrystallization textures.A higher manganese level was also detrimental to the development of the ND fibre components.These differences are attributed to variations in the dynamic strain aging and precipitation behaviors of the various materials attributable to their differing alloy contents.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 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".