Effect of Elements in Cu-enriched Liquid Phase on Surface Hot Shortness in Steels
Bibliographic record
Abstract
1.Introduction Recycling of steel has become important from the viewpoint of decreasing CO2 emission and conserving resources. Steel scrap contains tramp elements such as Cu, Sn and Ni. Copper in steel scrap causes surface cracking in hot working and obstructs promoting recycling of steel. When steel slab is heated in oxidation atmosphere for hot rolling, Cu-enriched phase is formed at the steel/scale interface through selective oxidation of Fe. The Cu-enriched phase penetrates into austenite (γ) grain boundaries during hot rolling, and causes surface cracking by liquid embrittlement. The phenomenon was called surface hot shortness due to Cu. It is difficult to remove Cu from steel scrap in the present separating or steelmaking technique. Therefore, the method for the suppression of the hot shortness is necessary is necessary to be developed. The authors investigated the effects of additive elements, grain size, atmosphere, temperature and deformation rate on the surface hot shortness, and proposed three methods for suppression of the hot shortness; (1) reducing the amount of the Cu-enriched phase, (2) restraining the penetration of Cu-enriched phase into γ grain boundaries, (3) refining γ grain size. For instance, the addition of minor content of B to a 0.1%C-0.5%Cu steel suppresses the hot shortness mainly through restraining the penetration. In this research, the effect of the elements such as B, P, Sn, Fe and Mn in Cu-enriched phase on the penetration into γ grain boundaries was investigated.
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) | 0.002 | 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".