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Record W2312602002 · doi:10.1115/ipc2012-90465

Formation of Martensite/Austenite (M/A) in X80 Linepipe Steel

2012· article· en· W2312602002 on OpenAlexafffund
Jennifer M. Reichert, Thomas Garcin, Matthias Militzer, Warren J. Poole

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsBainiteAusteniteMicrostructureMaterials scienceMetallurgyMartensiteFerrite (magnet)Continuous cooling transformationToughnessNiobiumMicroalloyed steelComposite material

Abstract

fetched live from OpenAlex

Linepipe steels are usually microalloyed with Nb to promote the formation of complex microstructures that lead to the required mechanical properties. In particular, Nb in solution affects significantly the austenite decomposition kinetics and the resulting microstructure. A systematic study has been carried out to quantify the influence of Nb on the austenite decomposition kinetics in X80 linepipe steel. Continuous cooling transformation tests were conducted with a Gleeble 3500. The transformation products include ferrite, granular and upper bainite and M/A (martensite/ retained austenite) constituents. For this study optical microscopy was used to investigate the formation of M/A constituents that critically determine the fracture toughness. A relation between M/A and the surrounding microstructure is observed. In combination with an existent model for the prediction of the microstructure evolution during weld thermal cycles, the area fraction, size and morphology of M/A can be predicted for the simulated HAZ, based on the prior austenite grain size, cooling rate and amount of Nb in solution.

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)0.000
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 score0.033
Threshold uncertainty score0.225

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.192
Teacher spread0.179 · 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

Citations19
Published2012
Admission routes2
Has abstractyes

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