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Record W4409296871 · doi:10.3390/ma18081701

Recrystallization Behavior of Cold-Rolled AA5083 Microalloyed with 0.1 wt.% Sc and 0.08 wt.% Zr

2025· article· en· W4409296871 on OpenAlexafffund
Ahmed Y. Algendy, Paul Rometsch, X.-Grant Chen

Bibliographic record

VenueMaterials · 2025
Typearticle
Languageen
FieldEngineering
TopicAluminum Alloy Microstructure Properties
Canadian institutionsRio Tinto (Canada)Université du Québec à Chicoutimi
FundersNatural Sciences and Engineering Research Council of CanadaCentre québécois de recherche et de développement de l’aluminium
KeywordsRecrystallization (geology)Materials scienceAnnealing (glass)AlloyElectron backscatter diffractionMetallurgyTransmission electron microscopyGrain boundaryDynamic recrystallizationMicrostructureScanning electron microscopeComposite materialHot workingNanotechnology

Abstract

fetched live from OpenAlex

The influence of annealing temperature on the mechanical properties, microstructural evolution, and recrystallization behavior of AA5083 cold-rolled sheets with and without Sc/Zr microalloying was studied utilizing hardness tests, optical microscopy, electron backscatter diffraction, and transmission electron microscopy. The results show that a minor addition of Sc/Zr to the Al-Mg-Mn alloy can significantly improve the alloy strength and recrystallization resistance. Adding 0.1 wt.% Sc and 0.08 wt.% Zr raised the recrystallization temperature of heavily deformed sheets to 500 °C, which is 250 °C higher than for the Sc-free base alloy. The higher recrystallization resistance of the Sc-bearing alloy was mainly attributed to the presence of Al3(Sc,Zr) nanoparticles, which enhanced the Zener drag pressure and delayed recrystallization. Grain boundary strengthening effects at various annealing temperatures were estimated using a constitutive equation. This work revealed that grain structure change and the corresponding boundary strengthening effect are key factors governing alloy strength evolution during annealing.

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 score0.008
Threshold uncertainty score0.576

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 head0.004
GPT teacher head0.179
Teacher spread0.176 · 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

Citations1
Published2025
Admission routes2
Has abstractyes

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