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Record W2884233844 · doi:10.5539/jmsr.v7n4p26

Relation between Sub-grain Size and Dislocation Density During Steady-State Dislocation Creep of Polycrystalline Cubic Metals

2018· article· en· W2884233844 on OpenAlexvenueno aff
Manabu Tamura

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldEngineering
TopicHigh Temperature Alloys and Creep
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceDislocationCreepCrystalliteGrain sizeAusteniteAlloyMartensiteMetallurgyAustenitic stainless steelCondensed matter physicsComposite materialMicrostructurePhysics

Abstract

fetched live from OpenAlex

The sub-grain size, d, during steady-state dislocation creep of polycrystalline metals is theoretically formulated to be inversely proportional to the dislocation density, ρ, which is defined as the number of dislocations swept out of a sub-grain divided by the cross-sectional area of the sub-grain. This dislocation density differs from the typically observed dislocation density inside a sub-grain after unloading, ρ_ob. In the current work, the ρ_ob values inside sub-grains in steadily crept specimens of Al, Cu, Fe, Fe–Mo alloy, austenitic stainless steel, and high-Cr martensitic steel reported in the literature were used to evaluate the relation ρ_ob=ηρ. It was confirmed that η≈1 for pure metals (regardless of the type of metal) crept at high temperatures and low stresses or for long durations and η>1 for Mo-containing alloys and martensitic steel crept at low temperatures and/or high stresses. Moreover, it is suggested that the condition η>1 corresponds to a state of excess immobile dislocations inside the sub-grain. The theoretical relation d_ob (≈d)∝η∙〖ρ_ob〗^(-1), where d_ob is the observed sub-grain size, essentially differs from the well-known empirical relation d_ob∝〖ρ_ob〗^(-0.5).

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)0.006
metaresearch head score (Gemma)0.001
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 score0.246
Threshold uncertainty score0.338

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
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 head0.025
GPT teacher head0.307
Teacher spread0.282 · 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

Citations6
Published2018
Admission routes1
Has abstractyes

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