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Study of Dynamic Precipitation during Hot Deformation of Mg-Al-Sn Alloys

2013· article· en· W2017442140 on OpenAlexafffund
Abu Syed Humaun Kabir, Jing Su, In‐Ho Jung, Stephen Yue

Bibliographic record

VenueMaterials science forum · 2013
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsMcGill University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceDynamic recrystallizationRecrystallization (geology)PrecipitationMetallurgyGrain sizeDeformation (meteorology)Strain rateAnnealing (glass)Hot workingComposite materialMicrostructureGeology

Abstract

fetched live from OpenAlex

The objective of this work is to investigate the effect of dynamic precipitation on the hot deformation behaviour of magnesium. Mg-Al-Sn alloys have been designed using thermodynamic modeling software, FactSage, based on precipitation of Mg 2 Sn at hot deformation temperatures. Uniaxial compression has been introduced at the temperature range of 250-350 °C to enhance the formation of precipitates. The flow behaviour and microstructural evolvement were studied for a strain rate of 0.01 /s and deformation degree of 90 %. Dynamic recrystallization (DRX) occurs in this study and it is found that the volume percent of dynamic recrystallization and dynamically recrystallized grain size are related to the amount of precipitation formed during deformation. The formation of Mg 2 Sn precipitates during the dynamic recrystallization process may retard the dynamic recrystallization and slow down the grain growth by precipitation pinning effect at the DRX grain boundaries, resulting in a finer grain size.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.072
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
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 head0.011
GPT teacher head0.246
Teacher spread0.235 · 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.

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

Citations7
Published2013
Admission routes2
Has abstractyes

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