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Record W1984269780 · doi:10.1155/2014/101872

Effect of Casting Parameters on the Microstructural and Mechanical Behavior of Magnesium AZ31-B Alloy Strips Cast on a Single Belt Casting Simulator

2014· article· en· W1984269780 on OpenAlexaff
Ahmad Changizi, Mamoun Medraj, Mihaiela Isac

Bibliographic record

VenueAdvances in Materials Science and Engineering · 2014
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsConcordia UniversityMcGill University
Fundersnot available
KeywordsMaterials scienceMetallurgySTRIPSComposite materialCastingMagnesium alloyHeat fluxCopperSubstrate (aquarium)MagnesiumHeat transfer

Abstract

fetched live from OpenAlex

Strips of magnesium alloy AZ31-B were cast on a simulator of a horizontal single belt caster incorporating a moving mold system. Mixtures of CO 2 and sulfur hexafluoride (SF 6 ) gases were used as protective atmosphere during melting and casting. The castability of the AZ31-B strips was investigated for a smooth, low carbon steel substrate, and six copper substrates with various textures and roughnesses. Graphite powder was used to coat the substrates. The correlation between strip thickness and heat flux was investigated. It was found that the heat flux from the forming strip to the copper substrate was higher than that to the steel substrate, while coated substrates registered lower heat fluxes than uncoated substrates. The highest heat flux from the strip was recorded for casting on macrotextured copper substrates with 0.15 mm grooves. As the thickness of the strip decreased, the net heat flux decreased. As the heat flux increased, the grain sizes of the strips were reduced, and the SDAS decreased. The mechanical properties were improved when the heat flux increased. The black layers which formed on the strips’ surfaces were analyzed and identified as nanoscale MgO particles. Nano-Scale particles act as light traps and appeared black.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.002
Threshold uncertainty score0.438

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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 head0.010
GPT teacher head0.234
Teacher spread0.224 · 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

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

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