Influence of Laves Phase Precipitation on Material Degradation of W Alloyed 9%Cr Ferritic Steel during Creep.
Bibliographic record
Abstract
The influence of Laves phase precipitation on a fracture process and creep rupture strength of W alloyed 9%Cr ferritic steel was investigated. Additionally, the change in strengthening factors during creep was examined by Vickers hardness and nano-indentation tests in order to clarify the material degradation mechanism. The main results obtained are as follows.(1) Laves phase is the preferred site for cavity to initiate. This cavity initiation at Laves phase and subsequent small crack formation cause the fracture of long-termed creep specimen.(2) The creep rupture strength at 600°C decreases with pre-aging at 650°C. Laves phase is closely associated with the decrease in the rupture strength, because the rupture time decrease as the amount of Laves phase increases.(3) Nano-indentation testing technique revealed that the matrix softening during thermal aging was caused by the decrease in the amount of W and Mo in solid solution due to Laves phase precipitation. This decrease in solidsolution strengthening due to Laves phase precipitation causes the above-mentioned decrease in the rupture strength.(4) The annihilation of dislocation is the predominant factor of the matrix softening in the transient creep region, while the matrix hardness decreases as the amount of W and Mo in solid solution decreases with Laves phase precipitation after the transient region.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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) | 0.000 | 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 teacher head, 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".