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Record W2003187360 · doi:10.1016/j.actamat.2014.07.018

In situ quantification of the nucleation and growth of Fe-rich intermetallics during Al alloy solidification

2014· article· en· W2003187360 on OpenAlexaff
Chedtha Puncreobutr, A.B. Phillion, Julie L. Fife, Peter Rockett, Andrew P. Horsfield, Peter Lee

Bibliographic record

VenueActa Materialia · 2014
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversity of British Columbia, Okanagan CampusUniversity of British Columbia
FundersEngineering and Physical Sciences Research CouncilCompetence Centre for Materials Science and TechnologyFord Motor Company
KeywordsIntermetallicNucleationMaterials scienceAlloyEutectic systemSupercoolingMetallurgyAluminiumSynchrotronAluminium alloyThermodynamics

Abstract

fetched live from OpenAlex

Real-time in situ synchrotron X-ray tomographic microscopy was used to gain new insights into and quantify the nucleation mechanisms and growth kinetics of β-Al5FeSi intermetallics during solidification of an aluminium Al–7.5Si–3.5Cu–0.6Fe (wt.%) alloy. Three new insights were obtained. First, the plate-like β-intermetallics appeared to nucleate mainly on or near the primary aluminium dendrites and to a lesser extent off the oxide skin on the surface of the specimen. Second, for this alloy composition, β-intermetallic formation was largely complete before the formation of Al–Si eutectic. Third, the β-intermetallics formed via fast lateral growth, wrapping around and in between the primary dendrite arms. Further, the nucleation and growth dynamics of β-intermetallics were quantified as a function of undercooling in a functional form that could be easily used in microstructural simulations. The frequency of intermetallic interaction mechanisms, such as plate nucleation vs. impingement and branching, were also quantified.

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 score0.002
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 head0.008
GPT teacher head0.199
Teacher spread0.191 · 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

Citations154
Published2014
Admission routes1
Has abstractyes

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Same venueActa MaterialiaSame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207