MétaCan
Menu
Back to cohort
Record W1977710303 · doi:10.1088/1757-899x/82/1/012028

Textures and Microstructures Formed in WE43 and AZ31 Magnesium Alloys during High Speed Rolling and Their Formation Mechanisms

2015· article· en· W1977710303 on OpenAlexaff
Yusuke Onuki, Kenichiro Hara, Hiroshi Utsunomiya, Jerzy A. Szpunar

Bibliographic record

VenueIOP Conference Series Materials Science and Engineering · 2015
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsMicrostructureMaterials scienceCrystal twinningMetallurgyTexture (cosmology)Magnesium alloyMagnesiumYttriumOxideComputer science

Abstract

fetched live from OpenAlex

High speed rolling is recognized as the process that can produce sheets of magnesium alloys having RD-split basal texture without or with minimum preheating. However, the mechanism of the texture formation during high speed rolling has not been fully clarified yet. In this study, conventional AZ31 and a rare earth - yttrium added alloy, WE43 were rolled with high rolling speed. The specimens having different textures were prepared by changing the cutting geometry to initially textured sheets. It is seen that the crack, microstructure and texture formations are strongly influenced by the initial textures in AZ31. These features are strongly related to the extension twinning, {10-12}<-1011>. In the case of WE43, cracks are formed more often than in AZ31, despite of the weak initial textures. It is proposed that the activities of the contraction and double twinning systems give more chance of stress concentration, resulting in the narrow shear banding and subsequent cracking. In addition to the experimental analysis, results of the numerical simulation using VPSC model are also used to discuss the texture formation mechanism.

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 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 score0.008
Threshold uncertainty score0.846

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 communication0.0010.001
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 head0.013
GPT teacher head0.194
Teacher spread0.181 · 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

Citations3
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueIOP Conference Series Materials Science and EngineeringSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207