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Record W2321832237 · doi:10.1515/secm.2011.006

Thermal stresses in rings of thermoplastic composites made by automated fiber placement process

2011· article· en· W2321832237 on OpenAlexafffund
Qi Zhao, Suong V. Hoa, Zhan Jun Gao

Bibliographic record

VenueScience and Engineering of Composite Materials · 2011
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialThermoplastic compositesThermalThermoplasticComposite numberHeat transferResidual stressFiberTransient (computer programming)CylinderMechanical engineeringMechanicsComputer scienceEngineering

Abstract

fetched live from OpenAlex

Abstract The automated fiber placement (AFP) of thermoplastic composites has a complex thermal history, which seriously leads to residual stresses in the products. An ANSYS simulation model is proposed to predict thermal history and induced thermal stresses in thermoplastic composite rings. It attempts to simulate closely the AFP process by adding materials incrementally. The processing speed was modeled by incrementally moving the hot air heater along the outer surface of the composite cylinder. The analysis is time-dependent, including two steps of transient heat transfer and induced thermal stress analyses. The heat transfer characteristics and the accumulated thermal effect during processing are investigated. Thermally induced stresses are examined.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

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)0.0010.000

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 head0.010
GPT teacher head0.216
Teacher spread0.207 · 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

Citations13
Published2011
Admission routes2
Has abstractyes

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