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Record W3025613379 · doi:10.1080/20550340.2020.1764236

Determination of convective heat transfer coefficient for automated fiber placement (AFP) for thermoplastic composites using hot gas torch

2020· article· en· W3025613379 on OpenAlexaff
Omid Aghababaei Tafreshi, Suong V. Hoa, Farjad Shadmehri, Duc Minh Hoang, Daniel Rosca

Bibliographic record

VenueAdvanced Manufacturing Polymer & Composites Science · 2020
Typearticle
Languageen
FieldEngineering
TopicHeat Transfer Mechanisms
Canadian institutionsConcordia University
Fundersnot available
KeywordsMaterials scienceNozzleHeat transferHeat transfer coefficientConvective heat transferComposite materialTorchConvectionHeat fluxVolumetric flow rateComposite numberThermodynamicsMechanicsPhysics

Abstract

fetched live from OpenAlex

In heat transfer analysis of AFP process using a hot gas torch, the convective heat transfer which occurs between the hot gas flow generated by a torch nozzle and a composite substrate plays an important role in the heat transfer mechanism. In order to model the convective heat transfer, a local heat flux equation q″=h (ΔT) is utilized where q″ is the energy flow per unit of area per unit of time, h is the convective heat transfer coefficient between the hot gas torch and the composite surface, and ΔT accounts for the temperature difference between the two media. This coefficient h is dependent on various number of parameters such as nozzle geometry and its configuration relative to the surface of the substrate, type and configuration of the roller, gas flow rate, temperature of the gas, type of the gas etc. Researchers on the heat transfer analysis for automated composites manufacturing have used values of h that vary from 80 W/m2K to 2500 W/m2K. This large range gives rise to uncertainties in the determination of important behavior such as the temperature distributions, residual stresses, and deformations of the composite structures due to the manufacturing process. The reason for these large differences can be due to the differences in the process parameters in each of the studies. The process parameters can include the volume flow rate of the hot gas, the gas temperature, the distance between the nozzle exit and the surface of the composite plate, the angle of the torch with respect to the surface of the substrate etc. In addition, the value of the h coefficient may not be constant over the heating length of the process. The purpose of this paper is three fold: 1. To investigate the AFP process parameters that may affect h. 2. To investigate different methods for the determination of h, and 3. To develop a procedure for less-time-consuming determination of h for the purpose of analysis for residual stresses and deformations.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.004
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.001
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 head0.018
GPT teacher head0.257
Teacher spread0.239 · 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 source (direct Gemma or distilled Codex), 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

Citations20
Published2020
Admission routes1
Has abstractyes

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