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MagForge – Mechanical Behaviour of Forged AZ31B Extruded Magnesium in Monotonic Compression

2015· article· en· W2246498403 on OpenAlexafffund
Andrew Gryguć, Hamid Jahed, Bruce W. Williams, Jonathan McKinley

Bibliographic record

VenueMaterials science forum · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Waterloo
FundersNatural Sciences and Engineering Research Council of CanadaFord Motor Company
KeywordsMaterials scienceCrystal twinningExtrusionForgingMagnesium alloyMonotonic functionSlip (aerodynamics)Deformation mechanismUltimate tensile strengthStrain hardening exponentHardening (computing)MetallurgyComposite materialCompression (physics)MagnesiumDeformation (meteorology)ThermodynamicsMicrostructure

Abstract

fetched live from OpenAlex

Monotonic compression testing was conducted on AZ31B-F magnesium alloy in both the as-received and forged conditions. Sigmoidal stress strain behaviour was the key feature in the majority of material conditions and directions corresponding to plastic behaviour where twinning de-twinning is the dominant deformation mechanism. More conventional monotonic hardening (slip deformation mechanism) was exhibited in certain material directions which initially were orthogonal to the extrusion direction in the as-received condition, but once forged are coincident with the direction of forging once forged. It was shown that in the forged condition, there is potential for significant increases in both ultimate tensile strength as well as strain to failure.

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)0.001
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 science0.0010.000
Research integrity0.0010.000
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 head0.029
GPT teacher head0.272
Teacher spread0.243 · 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

Citations17
Published2015
Admission routes2
Has abstractyes

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