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Record W3217561762 · doi:10.5539/jmsr.v11n1p1

Root Cause of Degradation in the Creep Strength of Martensitic Steel

2021· article· en· W3217561762 on OpenAlexvenueno aff
Manabu Tamura

Bibliographic record

VenueJournal of Materials Science Research · 2021
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsnot available
Fundersnot available
KeywordsCreepMaterials scienceLaves phaseMartensiteGrain boundaryPrecipitationPhase (matter)MetallurgyTransient (computer programming)Composite materialMicrostructureAlloy

Abstract

fetched live from OpenAlex

Creep curves of Grade 91 and 92 steels were analyzed by applying an exponential law to the temperature, stress, and time parameters to investigate the formation process of the Z-phase, which lowers the long-term rupture strength of high-Cr martensitic steel. 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 because of dislocations that are swept out of the sub-grains, and this is followed by strengthening owing to the rearrangement of M23C6 and the precipitation of the Laves phase. Heterogeneous recovery and subsequent heterogeneous deformation start at an early stage of transient creep near several of the weakest boundaries because of the coarsening of the precipitates; this results in the simultaneous decreases in Q, V, and C even in transient creep. Further, this activity triggers an unexpected degradation in strength because of the accelerated formation of the Z-phase even in transient creep. The stabilization of M23C6 and the Laves phase is important to mitigate the degradation of the long-term rupture strength of high-strength martensitic steel. The stabilization of the Laves phase is especially important for Cr-Mo systems because Fe2Mo is easily coarsened at approximately 600 °C compared to Fe2W in Grade 92 steel.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.035
Threshold uncertainty score0.187

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.046
GPT teacher head0.332
Teacher spread0.286 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2021
Admission routes1
Has abstractyes

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