Strength and Temper Embrittlement of Heavy-Section 2[fraction one-quarter]Cr-1Mo Steel
Bibliographic record
Abstract
The effects of austenitizing temperature and manganese, silicon, and phosphorus contents on temper embrittlement of 2 1 / 4 Cr-1Mo steel associated with its strength were studied using laboratory heats. High manganese content or high austenitizing temperature improves the strength and the initial toughness by suppressing the formation of ferrite. Once a fully bainitic structure is obtained, the increase in strength and the decrease in toughness are small even if the manganese content or the austenitizing temperature increases. The manganese content should be as low as possible under the condition where the fully bainitic structure is obtained for toughness in the step-cooled condition. The increase in susceptibility to temper embrittlement caused by a high austenitizing temperature is small. It may be compensated for by reducing manganese, silicon, or phosphorus content. The addition of silicon, intended to obtain higher strength, must be compensated for by lowering the phosphorus content to decrease the susceptibility to temper embrittlement. On the basis of the laboratory experiments, a 2 1 / 4 Cr-1Mo steel forging with a thickness of 397 mm was produced. The forging exhibited a good combination of strength and toughness in the step-cooled condition.
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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".