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Record W2021516781 · doi:10.5539/mas.v8n6p47

Kinetics of Dynamic Recrystallization in AA2024 Aluminum Alloy

2014· article· en· W2021516781 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

venuePublished in a venue whose home country is Canada.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueModern Applied Science · 2014
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsnot available
FundersShanghai Municipal Education Commission
KeywordsDynamic recrystallizationMaterials scienceFlow stressRecrystallization (geology)AlloySofteningMetallurgyAluminiumStrain rateHot workingKineticsStrain hardening exponentComposite materialGeology

Abstract

fetched live from OpenAlex

Understanding the deformation mechanism of aluminum alloys is very important for the transportation and aerospace industries. In the present study, the flow stress-strain curves for AA2024 aluminum alloy were obtained by compressive tests performed on Gleeble-3500 thermo-mechanical machine at temperatures from 250 to 450 °C, strain rates from 0.01 to 10 s-1. The dynamic recrystallization (DRX) kinetics was analyzed and the flow stress model characterizing dynamic recrystallization for AA2024 aluminum alloy was put forward. The flow stress curve at elevated temperature was described as three stages, i.e. hardening, softening and stable stages. The characteristic parameters for dynamic recrystallization in the flow stress model were described as function of Zener-Hollomon parameter. By analyzing the dynamic recrystallization region of flow stress curve, the kinetics of dynamic recrystallization was revealed.

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.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.797
Threshold uncertainty score0.274

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 head0.006
GPT teacher head0.202
Teacher spread0.196 · 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