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Record W3128049909 · doi:10.3390/ma14040762

Formation of Phases and Microstructures in Al-8Si Alloys with Different Mg Content

2021· article· en· W3128049909 on OpenAlexaff
Ni Tian, Guangdong Wang, Yiran Zhou, Chuncheng Liu, Kun Liu, Gang Zhao, Liang Zuo

Bibliographic record

VenueMaterials · 2021
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersFundamental Research Funds for the Central UniversitiesNational Key Research and Development Program of ChinaNational Natural Science Foundation of China
KeywordsEutectic systemMaterials scienceMicrostructureAlloyAluminiumMetallurgySilicon

Abstract

fetched live from OpenAlex

Mg-containing high-silicon aluminum alloy is a heat-treatable aluminum alloy that is now widely used in the aerospace and automotive industries because of its high specific strength, high wear resistance and corrosion resistance, low thermal expansion coefficient, and low cost. More attention has been paid to optimizing the microstructure to increase the performance of this type of aluminum alloy. In the present work, the solidification processes of Mg-free and Mg-containing (0.33–1.32%) Al-8Si alloys were analyzed by the experimental results combined with the thermodynamic calculation. The results showed that α-Al, Si, and Al5FeSi were in the Mg-free Al-8Si alloy ingots, while the Al5FeSi phases in alloys with Mg additions were transformed into π phases (Al8Mg3FeSi6) by the reaction L+Al5FeSi→α-Al+Si+Al8Mg3FeSi6. There was a binary eutectic reaction of L→α-Al+Al5FeSi when the Mg content exceeded 0.51% and the Fe content was higher than 0.17%. With the increase of Mg content, the volume of Mg2Si was gradually increased while the divorced eutectic phenomenon of the quaternary eutectic structure (α-Al+Si+Mg2Si+Al8Mg3FeSi6) was weakened and the eutectic structure was significantly refined.

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 score0.015
Threshold uncertainty score0.371

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 head0.014
GPT teacher head0.193
Teacher spread0.179 · 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

Citations15
Published2021
Admission routes1
Has abstractyes

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