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Formation of intermetallic phases during solidification in Al-Mg-Mn 5xxx alloys with various Mg levels

2020· article· en· W3094897945 on OpenAlexaff
Ahmed Y. Algendy, Kun Liu, X.-Grant Chen

Bibliographic record

VenueMATEC Web of Conferences · 2020
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité du Québec à Chicoutimi
Fundersnot available
KeywordsIntermetallicMaterials scienceEutectic systemAlloyDifferential scanning calorimetryScanning electron microscopeMetallurgyMicrostructurePhase (matter)ThermodynamicsChemistryComposite material

Abstract

fetched live from OpenAlex

In the present study, four Al-Mg-Mn 5xxx alloys with different Mg levels (2-5 wt.%) were investigated for better understanding the evolution of intermetallic phases formed during solidification. Optical and scanning electron microscopes, electron backscattered diffraction and differential scanning calorimetry analyses in combination with thermodynamic calculation were used to identify various intermetallic phases. Results showed that the most dominant intermetallic phases are Al6(Mn,Fe), α-Al(Fe,Mn)Si, Al3Fe, Alm(Mn,Fe) and Mg2Si in experimental Al-Mg-Mn alloys, which is greatly dependant on the Mg levels. It is found that Chinese script α-Al(Fe,Mn)Si is the dominant iron-rich intermetallic phase for the alloys containing 2-3 wt.% Mg, while blocky Al6(Mn,Fe) and needle-like Al3(Mn,Fe) become the major phases for the alloy containing 4 wt.% Mg. Further increasing Mg content to 5 wt. %, the dominant phase transfers to blocky Al6(Mn,Fe) intermetallic. Meanwhile, the morphology of primary Mg2Si is changed from well-branched to plate-like with increasing Mg contents. In addition, β-Al3Mg2 and τ-Al6CuMg4 eutectic phases have been observed in the alloys with 3-5 wt. % Mg. A comparison on various intermetallic phases from the Scheil simulation and the actual as-cast microstructure is provided.

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 score0.002

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 head0.025
GPT teacher head0.215
Teacher spread0.190 · 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".

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Citations33
Published2020
Admission routes1
Has abstractyes

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