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Record W2034900511 · doi:10.2320/matertrans.42.2050

Cube Texture Development in an Al-Mg-Mn Alloy Sheet Worked by Continuous Cyclic Bending

2001· article· en· W2034900511 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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueMATERIALS TRANSACTIONS · 2001
Typearticle
Languageen
FieldEngineering
TopicMetal Forming Simulation Techniques
Canadian institutionsMcGill University
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)Electron backscatter diffractionAlloyMetallurgySurface layerSurface finishComposite materialLayer (electronics)Microstructure

Abstract

fetched live from OpenAlex

Changes in texture after the continuous cyclic bending (CCB) and the subsequent annealing in sheets of an Al–4.7 mass%Mg–0.7 mass%Mn alloy have been investigated. The CCB was recently proposed as a straining technique that generates a high strain on the surface and a much lower strain in the central layer of the sheet. The Cube texture in the surface layer is sharpened remarkably during the CCB process and the annealing that follows. The 50 CCB passes lead to a sharper texture in all layers of the sheet. After annealing, marked development of the Cube component is observed in the surface layer. On the other hand, for the 20 pass-CCBent sample, the Cube texture appears only after annealing in a salt bath, while this texture is not observed after annealing both in Ar and in air. The mechanism of texture formation and the effect of processing on the sharpening of Cube texture is discussed based on results obtained from the electron backscatter diffraction pattern (EBSP) analysis and from the in-situ measurement of X-ray peak intensity during heating.

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 categoriesInsufficient payload (model declined to judge)
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 score0.120
Threshold uncertainty score0.999

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)0.0010.000

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 head0.012
GPT teacher head0.244
Teacher spread0.233 · 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