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Record W4387139338 · doi:10.3390/engproc2023043043

A Short Review on Superplasticity of Aluminum Alloys

2023· review· en· W4387139338 on OpenAlexafffund
Eric Kojo Kweitsu, D.K. Sarkar, X.-Grant Chen

Bibliographic record

Venuenot available
Typereview
Languageen
FieldMaterials Science
TopicMicrostructure and mechanical properties
Canadian institutionsUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologies
KeywordsSuperplasticityGrain Boundary SlidingMaterials scienceMetallurgyStrain rateCreepDeformation (meteorology)AlloyGrain sizeGrain boundaryDeformation mechanismComposite materialMicrostructure

Abstract

fetched live from OpenAlex

Superplastic aluminum (Al) alloys can be used in the forming processes to fabricate complex geometry components for a wide range of applications in the automobile industry, where light weight and high stiffness are needed. Those alloys exhibit extreme tensile elongation of more than 300% at a high homologous temperature and appropriate low strain rate. Superplasticity occurs in Al alloys via the mechanisms of grain boundary sliding, solute drag creep and diffusion creep. Grain boundary sliding usually leads to extensive superplasticity. The activation of grain boundary sliding depends on grain size, strain rate sensitivity, deformation temperature and alloy chemical composition. A complete understanding of influencing factors on Al alloy superplasticity is the key to developing novel superplastic Al alloys. This review discusses the superplastic behavior of several Al alloys, especially focusing on Al-Mg 5xxx alloys. It highlights the mechanisms that govern superplasticity of Al alloys at a low and high strain rate. The factors which influence superplasticity are analyzed. As practice industrial applications, high-cycle-time superplastic forming operations such as quick plastic forming and high-speed blow forming are briefly discussed.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.857
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.002

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 head0.121
GPT teacher head0.356
Teacher spread0.234 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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

Citations9
Published2023
Admission routes2
Has abstractyes

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