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Record W2792533556 · doi:10.1080/20550340.2018.1444535

Effect of in situ treatment on the quality of flat thermoplastic composite plates made by automated fiber placement (AFP)

2018· article· en· W2792533556 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

VenueAdvanced Manufacturing Polymer & Composites Science · 2018
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsConcordia University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsThermoplastic compositesMaterials scienceComposite numberComposite materialThermoplasticAutoclaveIn situAerodynamicsCrystallinityEngineeringAerospace engineeringMetallurgy

Abstract

fetched live from OpenAlex

Composite structures used as aerosurfaces in aerodynamic applications are required to have a certain surface finish quality. In manufacturing of thermoplastic composites using automated fiber placement (AFP) for aerodynamic applications, it is not only desirable to achieve good consolidation by using AFP alone and avoiding secondary treatment in an autoclave, but also to achieve acceptable surface smoothness required for aerosurfaces. In this study, an in situ treatment called “repass” was implemented to achieve surface finish quality required for aerodynamic applications. Moreover, the effect of this in situ treatment on the quality of the thermoplastic laminates, including void content and crystallinity was investigated. Autoclave-treated samples were used as references for comparing surface quality and other quality indicators.

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)
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 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
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.011
GPT teacher head0.282
Teacher spread0.270 · 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