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Record W1522264394 · doi:10.1063/1.1570332

Laser-Ultrasonic Characterization of the Annealing Process of Low-Carbon Steel

2003· article· en· W1522264394 on OpenAlexaff
Guy Lamouche

Bibliographic record

VenueAIP conference proceedings · 2003
Typearticle
Languageen
FieldEngineering
TopicMicrostructure and Mechanical Properties of Steels
Canadian institutionsNational Research Council Canada
Fundersnot available
KeywordsMaterials scienceAnnealing (glass)Recrystallization (geology)Ultrasonic sensorLaserMetallographyLaser ultrasonicsComposite materialUltrasoundMetallurgyMicrostructureOpticsOptoelectronicsAcousticsWavelengthGeology

Abstract

fetched live from OpenAlex

Laser‐ultrasonic measurements are performed during the annealing of cold‐worked low‐carbon steel at temperatures between 590°C and 610°C. During annealing, the ultrasound velocity behaves in a peculiar way, first decreasing and then increasing to a final value. The recrystallized fraction evaluated by metallography indicates that recrystallization occurs during both the decrease and the increase in longitudinal velocity. Texture evaluated using laser ultrasonics suggests that the peculiar behavior of the velocity might be due to two different regimes of texture evolution. Internal friction, investigated through ultrasound absorption measurements, is also revealed to be very sensitive to the annealing process. This internal friction is attributed to magnetomechanical effects that are also known to decrease the ultrasonic longitudinal velocity and probably contribute to the observed peculiar behavior of the velocity. From this work, we conclude that, although there remains much work to do to explain our observations, both the longitudinal velocity and internal friction measurements are promising approaches for the development of an online sensor to characterize annealing using laser‐ultrasonics.

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 score0.007
Threshold uncertainty score0.411

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 head0.010
GPT teacher head0.196
Teacher spread0.187 · 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

Citations3
Published2003
Admission routes1
Has abstractyes

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