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Record W4408342993 · doi:10.1177/08927057251321492

Understanding a frequency shifting phenomenon interfering with in-line acoustic monitoring of an extrusion compounding process for polymer composites

2025· article· en· W4408342993 on OpenAlexaff
Austin D. Bedrosian, Andrew N. Hrymak, Gisela Lanza, Michael R. Thompson

Bibliographic record

VenueJournal of Thermoplastic Composite Materials · 2025
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsWestern UniversityMcMaster University
FundersDeutsche Forschungsgemeinschaft
KeywordsCompoundingMaterials scienceExtrusionComposite materialPolymer

Abstract

fetched live from OpenAlex

This study investigated an anomalous frequency shift observed in collected spectra from an inline monitoring system based on guided ultrasonic waves, with the changing flow rate of an extrusion compounding process for fiber-reinforced thermoplastics. Three possible process parameters to explain the ultrasonic peak shifting, namely melt temperature, velocity, and fiber length were evaluated. The unlikely potential of a doppler moment due to melt velocity, was readily dismissed in the analysis since fluid flow through the die was too slow and while resonance frequency variation may be possible from the related fiber damage associated with increasing flow rates, there was insufficient physical evidence of this anticipated effect in this study. Melt temperature variation associated with viscous dissipation was concluded to be the dominant cause for the frequency shifting noted in the acoustic spectra. The changes in material temperature through which the sound travelled were varying the extent and frequency of the dispersion modes in the polymer melt. These findings are new guidance to processors on setting up a system using active ultrasonics for in-line monitoring in the polymer composites industry.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
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 score0.138
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
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 head0.049
GPT teacher head0.291
Teacher spread0.242 · 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.

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
Published2025
Admission routes1
Has abstractyes

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