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In-Situ, Laser-Ultrasonic Monitoring of the Recrystallization of Aluminum Alloys

2003· article· en· W2028070991 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 science forum · 2003
Typearticle
Languageen
FieldEngineering
TopicMetallurgy and Material Forming
Canadian institutionsUniversity of British ColumbiaNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceRecrystallization (geology)In situAluminiumMetallurgyUltrasonic sensorLaserOpticsAcoustics

Abstract

fetched live from OpenAlex

Laser-ultrasonics is a non-destructive and non-contact technique to generate and detect ultrasound in materials. The measured ultrasonic velocity depends on the orientation distribution of the crystallites and may be used to infer the lowest order texture coefficients. Recrystallization generally involves texture changes and can thus be monitored using ultrasonics. In this paper, cold-rolled samples of an Al-Mg alloy (AA5754) and an Al-Si-Mg-Cu alloy (AA6111) are annealed in a Gleeble thermomechanical simulator at various temperatures. The recrystallization kinetics is monitored in-situ and in real time by laser-ultrasonics. It is found that the longitudinal and shear velocity variations correlate well with the recrystallized fraction, as evaluated by metallography and by the softening behaviour of samples submitted to similar thermal cycles. it is also found that the ultrasonic behaviour is consistent with the randomization of texture and a reduction of the W400 and W420 texture coefficients.

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)0.001
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 score0.005
Threshold uncertainty score0.261

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.011
GPT teacher head0.224
Teacher spread0.213 · 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