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Record W3196556340 · doi:10.1080/19236026.2021.1949255

Characterization of AA6111 aluminum alloy thin strips produced via the horizontal single belt casting process

2021· article· en· W3196556340 on OpenAlexafffund
Usman Niaz, M. Isac, R. I. L. Guthrie

Bibliographic record

VenueCIM Journal · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceMicrostructureProfilometerIntermetallicUltimate tensile strengthAlloySTRIPSMetallurgyGrain sizeScanning electron microscopeCastingAluminiumSurface roughnessComposite materialSurface finishGrain boundary

Abstract

fetched live from OpenAlex

In this research, AA6111 aluminum alloy strips, 250 mm wide and ~6 mm thick, were produced via the horizontal single belt casting process. The strip microstructure was analyzed using optical and scanning electron microscopy, and the measured average grain size was 90 μm. This is significantly smaller than the average grain size of the products produced via direct chill (DC) casting route (i.e., 231 μm). An analysis of intermetallic compounds was also conducted using energy dispersive spectroscopy. These were observed to be distributed within and along the grain boundaries and are enriched in Mg, Si, and Cu. The shear punch test was performed to determine the ultimate tensile strength of the cast strip, which was observed to be 280 MPa. This value is significantly higher than the strength documented in the literature for DC cast products. A 3D profilometer was used to determine the top and bottom surface roughness of the as-cast AA6111 strips. The strip top surface was found to lie within 120 μm range, whilst the bottom surface roughness was less than 30 μm.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.012
GPT teacher head0.199
Teacher spread0.186 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2021
Admission routes2
Has abstractyes

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Same venueCIM JournalSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207