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Non-Isothermal Austenite Grain Growth Kinetics in the HAZ of a Microalloyed X-80 Linepipe Steel

2011· article· en· W2053351390 on OpenAlex

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A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueDiffusion and defect data, solid state data. Part B, Solid state phenomena/Solid state phenomena · 2011
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsAusteniteMaterials scienceMetallurgyIsothermal processMicroalloyed steelGrain growthGrain sizeKineticsMicrostructureThermodynamics

Abstract

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Non-isothermal austenite grain growth kinetics under the influence of several combinations of Nb, Ti and Mo containing complex precipitates has been studied in a microalloyed linepipe steel. The goal of these studies is the development of a grain growth model to predict the austenite grain size in the weld heat affected zone (HAZ). A detailed electron microscopic investigations of the as-received steel proved the presence of Ti-rich, Nb-rich and Mo-rich precipitates. Inter and intragranular precipitates of ~5-150 nm have been observed. The steel has been subjected to austenitizing heat treatments to selected peak temperatures of 950, 1150 and 1350°C at various heating rates of 10, 100 and 1000°C/s. Thermal cycles have been found to have a strong effect on the final austenite grain size. The increase in heating rate from 100 to 1000°C/s has a negligible difference in the austenite grain size irrespective of the austenitizing temperature. However, the increase in grain size has been noticed at 10°C/s heating rate for all the austenitizing temperatures. The austenite grain growth kinetics have been explained taking into account the austenite growth in the presence of precipitates.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesMeta-epidemiology (narrow)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.545
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0040.003
Research integrity0.0000.001
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 head0.037
GPT teacher head0.251
Teacher spread0.215 · 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