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Record W2930464785 · doi:10.2320/matertrans.l-m2020827

Relationship between Deep Drawability and Microstructure of Magnesium Alloy

2020· article· en· W2930464785 on OpenAlexaff
Yosuke Tamura, H. Soda, Alexander McLean

Bibliographic record

VenueMATERIALS TRANSACTIONS · 2020
Typearticle
Languageen
FieldMaterials Science
TopicMagnesium Alloys: Properties and Applications
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMaterials scienceMicrostructureMetallurgyMagnesium alloyMagnesiumAlloy5052 aluminium alloy

Abstract

fetched live from OpenAlex

Mg–1.5Zr alloy blanks 1.0 mm in thickness, made from cast and rolled materials, were deep-drawn at room temperature and 150°C. The microstructures were examined and deformability evaluated. The grain size of blanks from cast and rolled materials was 40 µm and 15 µm, respectively. The basal texture was confirmed for rolled-blanks, while the microstructures of cast blanks were randomly orientated. The limiting drawing ratio for cast materials was 1.65 at room temperature and 1.75 at 150°C. For rolled-blanks, it was 1.45 and 1.70 at room temperature and 150°C, respectively. The cast-blanks deformed in a similar way at room temperature and at 150°C, exhibiting a decrease in thickness in the punch-shoulder areas and a progressive increase along the side wall toward the rim of the cups. Rolled-blanks tested at 150°C showed the similar trend as cast-blanks and exhibited evidence of dynamic recrystallization in the punch-shoulder areas, contributing to an improvement on the limiting drawing ratio. When the deep-drawing exceeded the limiting drawing ratio, the cast-blanks tested at room temperature and 150°C and also the rolled-banks tested at 150°C fractured in the rim area of the cups, while the rolled-blanks at room temperature fractured from the punch-shoulder areas.

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 score0.001
Threshold uncertainty score0.003

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)0.0010.000

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 head0.027
GPT teacher head0.234
Teacher spread0.207 · 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

Citations3
Published2020
Admission routes1
Has abstractyes

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Same venueMATERIALS TRANSACTIONSSame topicMagnesium Alloys: Properties and ApplicationsFrench-language works237,207