MétaCan
Menu
Back to cohort

Inverse and centreline segregation formation in twin roll cast AZ31 magnesium alloy

2015· article· en· W1981893976 on OpenAlexaff
Amir Hadadzadeh, Mary A. Wells

Bibliographic record

VenueMaterials Science and Technology · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsMaterials scienceMicrostructureMagnesium alloyMetallurgyCasterInverseCastingFinite element methodComposite materialThermodynamicsGeometryMathematics

Abstract

fetched live from OpenAlex

Two microstructure defects formed in the twin roll cast AZ31 strips were investigated: inverse and centreline segregations. A two-dimensional finite element thermal–fluid–stress model was employed to study the thermomechanical response of the AZ31 strip during twin roll casting process. The results showed that the key parameter for centreline segregation is the mushy zone thickness at centreline. For inverse segregation, the interaction between the yy peak stress at the centreline in the mushy zone and the solidified shell on the roll surface is the determinant parameter. The modelling results suggested increasing the setback distance decreases the risk of both defects. Moreover, scaling up the caster reduces the propensity to inverse segregation but appears to have a minor effect for centreline segregation formation.

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 score0.002
Threshold uncertainty score0.004

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 head0.019
GPT teacher head0.231
Teacher spread0.212 · 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

Citations8
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueMaterials Science and TechnologySame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207