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Record W4200387138 · doi:10.1016/j.jcomc.2021.100214

Models for heat transfer in thermoplastic composites made by automated fiber placement using hot gas torch

2021· article· en· W4200387138 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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueComposites Part C Open Access · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialTorchComposite numberHeat transferThermalTransient (computer programming)Deformation (meteorology)ConvectionComposite laminatesMechanicsThermodynamicsComputer science

Abstract

fetched live from OpenAlex

This paper presents a 2D transient heat transfer model, which has been developed using finite difference method for composites made by AFP process with a hot gas torch. The model includes non-linear distributions for the hot gas/air temperature and convection coefficients in the vicinity of composite, which are essential to increase the accuracy of the model. It is necessary not to just obtain the accurate maximum temperature of composite, but more importantly, the thermal gradients in the composite laminate. These thermal gradients have strong influence on the development of residual stresses and deformation in thermoplastic laminates made by AFP. In addition, the temperature dependencies of the material properties and inter-layer thermal contact resistance have been taken into account. Comparison of the experimental and theoretical results indicates how the accuracy of model increases by considering the above mentioned aspects.

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 categoriesMeta-epidemiology (narrow), Scholarly communication
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 score0.236
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.001
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 head0.075
GPT teacher head0.349
Teacher spread0.274 · 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