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Record W4323041145 · doi:10.1080/00084433.2023.2185410

Influence of transition elements (Zr, V, and Mo) on microstructure and tensile properties of AlSi8Mg casting alloys

2023· article· en· W4323041145 on OpenAlexafffund
Zhan Zhang, Anil Arici, Francis Breton, X.-Gant Chen

Bibliographic record

VenueCanadian Metallurgical Quarterly · 2023
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsRio Tinto (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMicrostructureMaterials scienceUltimate tensile strengthEutectic systemIntermetallicPrecipitationMetallurgyElongationAluminiumCastingTransition temperatureComposite materialAlloy

Abstract

fetched live from OpenAlex

The influence of the additions of transition elements (Zr, V, and Mo) on microstructure, precipitation behaviour and tensile properties of Al–8%Si–0.3%Mg permanent mould castings were investigated. It reveals that the presence of the transition elements increased the solute concentration in aluminium matrix, refined eutectic Si particles, and reduced the length and aspect ratio of intermetallic phases in as-cast microstructure. Moreover, the transition element addition promoted the formation of solute cluster and increased the number density of β′ precipitates and dispersoids during T6 heat treatment. The additions of Zr, V, and Mo considerably improved the tensile strengths and elongation in both as-cast and T6 conditions. The effects of the combined addition of Zr, V, and Mo on the microstructure, precipitation behaviour, and tensile properties were stronger than that of the addition of Zr and V.

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 score0.557
Threshold uncertainty score0.704

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 head0.009
GPT teacher head0.181
Teacher spread0.172 · 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
Published2023
Admission routes2
Has abstractyes

Explore more

Same venueCanadian Metallurgical QuarterlySame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207