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Record W4206311269 · doi:10.29391/2021.100.030

Effect of the Heating Rate on Austenite Formation

2021· article· en· W4206311269 on OpenAlexafffund
ALEJANDRO HINTZE CESARO, Patricio F. Méndez

Bibliographic record

VenueWelding Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsCanadian VIGOUR Centre
FundersNatural Sciences and Engineering Research Council of CanadaAmerican Welding SocietyAmerican Welding Society Foundation
KeywordsAusteniteFerrite (magnet)Materials scienceMetallurgyWeldingBainiteWork (physics)Atmospheric temperature rangeContinuous cooling transformationThermodynamicsMicrostructureComposite material

Abstract

fetched live from OpenAlex

The extent of the heat-affected zone (HAZ) in welding is typically estimated from thermodynamic considerations of austenization; however, thermodynamics are a poor predictor of the HAZ location in microalloyed steels. This work addresses the problem through the study of austenite formation during continuous heating on a grade X80 pipeline steel with an initial ferritic and bainitic microstructure. The methodology involved dilatometry, electron microscopy, and thermodynamic calculations. A continuous heating transformation diagram was developed for heating rates varying from 1˚ to 500˚C/s. For the slower heating rates, austenite start-transformation temperature was higher than the one dictated by the equilibrium, while for the faster heating rates, start-transformation temperature gradually approached the theoretically calculated temperature at which the ferrite can transform (possibly through a massive transformation) without a long-range diffusion into austenite. Partial-transformation experiments suggested that austenite formation occurs in the following two stages: 1) the transformation of bainitic zones into austenite, and later, 2) the transformation of polygonal ferritic grains.

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 score0.056
Threshold uncertainty score0.142

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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 routes2
Has abstractyes

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