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Record W4404229731 · doi:10.3390/ma17225492

Effect of Annealing Time on Grain Structure Evolution and Superplastic Response of Al-Mg 5xxx Alloys

2024· article· en· W4404229731 on OpenAlexafffund
Eric Kojo Kweitsu, D.K. Sarkar, Ahmed Y. Algendy, X.-Grant Chen, Jocelyn Veilleux, Nicolas Bombardier

Bibliographic record

VenueMaterials · 2024
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsUniversité de SherbrookeUniversité du Québec à Chicoutimi
FundersFonds de recherche du Québec – Nature et technologies
KeywordsMaterials scienceSuperplasticityAnnealing (glass)MetallurgyMicrostructure

Abstract

fetched live from OpenAlex

The impact of annealing on the recrystallized grain structure and superplastic behavior of two Al-Mg 5xxx alloys used for high-speed blow forming (HSBF) was studied. The results revealed that both alloys demonstrated rapid static recrystallization after only a few minutes of annealing at 520 °C, forming fine and equiaxed grain structures. After four min of annealing, Alloy 2 (Al-4.0Mg-1.18Mn) exhibited a higher fraction of small grains (<10 µm) compared to Alloy 1 (Al-4.5Mg-0.74Mn). Moreover, Alloy 2 displayed enhanced resistance to grain coarsening with increasing annealing times, which was attributed to its higher amount of Al6(Mn,Fe) intermetallic particles and a higher number density of Mn dispersoids. Optimizing the annealing time can effectively develop a fine and stable grain structure in Al-Mg 5xxx alloys. During tensile deformation, Alloy 2 consistently showed higher ductility compared to Alloy 1 at low strain rates (170% vs. 138% at 0.001 s−1 and 163% vs. 134% at 0.01 s−1), whereas at a high strain rate of 1 s−1, both alloys displayed comparable tensile elongation. The high superplastic response of Alloy 2 at low strain rates renders it a superior superplastic alloy for HSBF applications.

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)0.000
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 score0.002
Threshold uncertainty score0.558

Codex and Gemma teacher scores by category

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 head0.003
GPT teacher head0.199
Teacher spread0.197 · 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

Citations5
Published2024
Admission routes2
Has abstractyes

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