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Record W4324045064 · doi:10.6000/1929-5995.2023.12.01

Dynamic Elastic Modulus Variability in Anisotropic and Isotropic Materials: Comparison by Acoustic Emission

2023· article· en· W4324045064 on OpenAlexvenueno aff
Henrique Pina Cardim, Larissa Queiroz Minillo, Fernando Nakao, Altibano Ortenzi

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2023
Typearticle
Languageen
FieldEngineering
TopicUltrasonics and Acoustic Wave Propagation
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceFilament windingComposite materialIsotropyAcoustic emissionAnisotropyElastic modulusLaminationModulusAngular displacementComposite numberAcousticsOpticsPhysics

Abstract

fetched live from OpenAlex

This study compared the variation of the dynamic elastic modulus (E) of three types of composite pipes made by the filament winding process and a steel alloy specimen, according to signal source changes. The specimens were produced with three different winding angles, i.e., ±50°, ±52.5°, and ±55°. The moduli were obtained through a known signal source and the angular variation, according to two sensors positioned over the specimen's surface. In a previous article, the variation in the velocity of acoustic emission (AE) signals, performed in the same type of pipes, was discussed based on the standards for glass fiber-reinforced epoxy (GFRE) filament wound specimens. This work took these preliminary findings to compare with the results found for steel alloy pipes (SAE 1020). This data was used with appropriate equations to determine the dynamic elastic moduli of each material. It was found that, even for small angular differences, the modulus changes position concerning the lamination angle. Thus, the lower the quality control, the lower the final product with composite materials. As expected, for isotropic materials such as steel alloys, the modulus remains constant along the angles, while for anisotropic ones, it is dependent on the principal directions of stress and strain, or on the other hand, dependent on the correlation between the angular wave velocity of the AE signals.

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)0.004
metaresearch head score (Gemma)0.001
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 score0.808
Threshold uncertainty score0.348

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
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 head0.015
GPT teacher head0.325
Teacher spread0.310 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

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