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Record W2776189965 · doi:10.2298/jmmb170512052c

Thermodynamic analysis of 6xxx series Al alloys: Phase fraction diagrams

2017· article· en· W2776189965 on OpenAlexafffund
Senlin Cui, R.K. Mishra, In‐Ho Jung

Bibliographic record

VenueJournal of Mining and Metallurgy Section B Metallurgy · 2017
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of CanadaMcGill University
KeywordsAlloyMaterials scienceMicrostructurePhase diagramThermodynamicsAnnealing (glass)MetallurgyMole fractionPhase (matter)Fraction (chemistry)Chemistry

Abstract

fetched live from OpenAlex

Microstructural evolution of 6xxx Al alloys during various metallurgical processes was analyzed using accurate thermodynamic database. Phase fractions of all the possible precipitate phases which can form in the as-cast and equilibrium states of the Al-Mg-Si-Cu-Fe-Mn-Cr alloys were calculated over the technically useful composition range. The influence of minor elements such as Cu, Fe, Mn, and Cr on the amount of each type of precipitate in the as-cast and equilibrium conditions were analyzed. Phase fraction diagrams at 500 ?C were mapped in the composition range of 0-1.1 wt.% Mg and 0-0.7 wt.% Si to investigate the as-homogenized microstructure. In addition, phase fraction diagram of Mg2Si at 177 ?C was mapped to understand the microstructure after final annealing of 6xxx Al alloy. Based on the calculated diagrams, the design strategy of 6xxx Al alloy to produce highest strength due to Mg2Si is discussed.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.257
Teacher spread0.242 · 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 designSimulation or modeling
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

Citations10
Published2017
Admission routes2
Has abstractyes

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Same venueJournal of Mining and Metallurgy Section B MetallurgySame topicAluminum Alloy Microstructure PropertiesFrench-language works237,207