Analysis of the Degradation in the Creep Strength of High-Cr Martensitic Steels
Bibliographic record
Abstract
To investigate the formation process of the Z-phase, which lowers the long-term rupture strength of high-Cr martensitic steel, the creep curves of Grades T91, T92, and P92 were analyzed along with the experimental steels of 9Cr-1W and 9Cr-4W by applying an exponential law to the temperature, stress, and time parameters. The activation energy (Q ), activation volume (V ), and Larson-Miller constant (C ) were obtained as functions of creep strain. At the beginning of creep, sub-grain boundary strengthening occurs due to dislocations that are swept out of the sub-grains, which is followed by strengthening due to the rearrangement of M23C6 and the precipitation of the Laves phase. After Q reaches a peak, heterogeneous recovery and subsequent heterogeneous deformation begin at an early stage of transient creep in the vicinity of several of the weakest boundaries due to coarsening of the precipitates. This activity triggers an unexpected degradation in strength due to the accelerated formation of the Z-phase. Stabilization of M23C6 and the Laves phase is important for mitigating the degradation of the long-term rupture strength of high-strength martensitic steel. The stabilization of the Laves phase is especially important for the Cr-Mo systems because Fe2Mo is easily coarsened at ~600 °C as compared to Fe2W. Lowering the hardness and Si content also prevents excess hardening due to the Laves phase, which also mitigates the degradation. The online monitoring of creep curves and the QVC analysis render it possible to detect signs of long-term degradation under targeted conditions within a relatively short period.
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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".