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Record W2089477756 · doi:10.1590/1516-1439.261814

An investigation into the dissolution characteristics of γ precipitates in Mg-3Al-Zn alloy

2014· article· en· W2089477756 on OpenAlexaff
Javad Mohammadi, M Ghoreishi, Yashar Behnamian

Bibliographic record

VenueMaterials Research · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsEutectic systemMaterials scienceAlloyDissolutionMetallurgyMicrostructureMelting pointPhase (matter)Magnesium alloyAtmospheric temperature rangeThermodynamicsComposite materialChemical engineeringChemistry

Abstract

fetched live from OpenAlex

In non-equilibrium conditions, the coring phenomena may occur in the α-Mg solid solution and γ-Mg17Al12 phase forms in the microstructure of AZ magnesium alloy series. This eutectic phase may introduce detrimental effect on workability of wrought alloys. In the present work, the dissolution characteristics of γ phase have been investigated in AZ31 wrought magnesium alloy. Considering the eutectic temperature of AZ31 alloy, the homogenization treatment was executed in temperature range of 300-437 ºC (i.e., somewhat below eutectic melting temperature of γ precipitate) and 437-500 ºC (i.e., higher eutectic melting temperature of γ precipitate) for different durations. A thorough microstructural investigation and also thermodynamic calculations were executed. The results indicated that the dissolution rate of γ phase is very low even at temperatures just below the eutectic point of the alloy, whereas it dramatically increases at temperatures higher than eutectic point. Moreover, it is suggested that partial melting of γ phase due to eutectic melting reaction (α+γ→L) may lead to reduced formability.

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 score0.003
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
Bibliometrics0.0000.000
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 head0.048
GPT teacher head0.334
Teacher spread0.285 · 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".

Quick stats

Citations14
Published2014
Admission routes1
Has abstractyes

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