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Record W3154190588 · doi:10.1080/00084433.2021.1910426

A microstructural and damage investigation into the effect of coiling temperature on the low temperature impact behaviour of an HSLA structural steel

2021· article· en· W3154190588 on OpenAlexafffund
Siwei Wu, Alexander Bardelcik, Tihe Zhou, Peter Badgley, Chad Cathcart

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2021
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsUniversity of Guelph
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsCharpy impact testMaterials scienceVoid (composites)MicrostructureCarbideFerrite (magnet)PearliteMetallurgyGrain boundaryBrittlenessNucleationComposite materialAusteniteThermodynamics

Abstract

fetched live from OpenAlex

An HSLA grade of structural steel was hot rolled using a coiling temperature of 500°C (CT500) or 540°C (CT540). As expected, the lower coiling temperature resulted in a more refined ferrite grain structure and a more dispersed distribution of smaller pearlite grains and carbides which formed along grain boundaries. Instrumented Charpy V-notch (CVN) tests were conducted and the CT500 material resulted in a higher ductile-brittle transition temperature, which was unexpected for the more refined microstructure. Damage evolution was characterized from the tested CVN specimens and revealed that a higher initial void/inclusion content for CT500 resulted in elevated void area fraction and void density values near the fracture surface of this material. Also, the refined distribution of carbides contributed to the higher void nucleation rate of CT500, which ultimately reduced the low temperature impact performance of this material.

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 score0.051
Threshold uncertainty score0.652

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 integrity0.0000.001
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 head0.004
GPT teacher head0.201
Teacher spread0.197 · 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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