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Record W3180520130 · doi:10.5539/jmsr.v10n1p1

Analysis on Degradation in Creep Strength of 9Cr-W Martensitic Steel

2021· article· en· W3180520130 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 scienceMartensiteLaves phaseGrain boundaryPrecipitationMetallurgyPhase (matter)Deformation (meteorology)Composite materialMicrostructureAlloy

Abstract

fetched live from OpenAlex

In order to clarify the creep mechanism of high Cr martensitic steel, creep curves of 9Cr-1W and 9Cr-4W steels were analyzed applying an exponential law to the temperature, stress, and time parameters. The activation energy, Q, the activation volume, V, and the Larson-Miller constant, C, are obtained as functions of creep strain. At the beginning of creep, sub-grain boundary strengthening by swept dislocations out of sub-grains occurs followed by strengthening due to the rearrangement of M23C6 and the precipitation of 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 some weakest boundaries due to coarsening of the precipitates, which triggers the unexpected degradation in strength due to the accelerating coarsening of precipitates. Stabilizing not only M23C6 but also Laves phase is important to mitigate the degradation of rupture strength of martensitic steel. The above creep mechanism for martensitic steel can be applicable to the explanation for the degradation in long term rupture strength of high Cr martensitic steel, Grades 91 and 92.

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)0.004
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 score0.050
Threshold uncertainty score0.226

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
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 head0.033
GPT teacher head0.327
Teacher spread0.294 · 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

Citations3
Published2021
Admission routes1
Has abstractyes

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