INVESTIGATIONS ON THE ROLE OF COPPER IN CAUSING AND NICKEL IN PREVENTING THE “HOT SHORTNESS” IN STEELS
Bibliographic record
Abstract
An extensive investigation into the role of low copper contents on the hot ductility of C-Mn-Al steels has been carried out in order to better understanding the problem of "hot shortness".Previous work has suggested that this problem results from the build up of Cu that occurs at the surface of the steel as a consequence of the preferential oxidation of iron.This causes the formation of a Cu-rich film of low melting point.A nickel addition has been reported as a solution to the problem, since it increases the solubility of copper in the austenite.After hot tensile testing to failure, samples of Cucontaining steels have been examined using optical, scanning and transmission electron microscopes.Recently published results indicate that copper, in addition to precipitating out as CuS, also segregates to MnS inclusions forming a shell around them.This does not seem to impair the hot ductility under an inert atmosphere, but may have serious consequences under an oxidising environment and lead to "hot shortness".The influence of nickel in improving the hot ductility seems to be due to it forming a higher melting point alloy with the segregated copper.Data from this incomplete work also suggests that Ni reduces the precipitation of CuS particles.Current investigation carried out using confocal microscopy still to be completed will improve the understanding of the role of copper and nickel on the problem of "hot shortness".
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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 | 0.001 | 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".