MétaCan
Menu
Back to cohort
Record W3031590260 · doi:10.4236/msa.2020.115022

Thermo-Stamping Process of Glass and Carbon-Fibre Reinforced Polymer Composites

2020· article· en· W3031590260 on OpenAlex
Walid Harizi, Zoheir Aboura, M. Deléglise, Valérie Briand

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.

Bibliographic record

VenueMaterials Sciences and Applications · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsSafran Electronics (Canada)
FundersEuropean Regional Development FundRégion NormandieRégion Hauts-de-France
KeywordsPolyetherimideMaterials scienceComposite materialScanning electron microscopeThermoplasticPorosityWettingPolymerCarbon fibersComposite number

Abstract

fetched live from OpenAlex

In this work, manufacturing tools for thermoplastic (TP) composites have been developed. The chosen process involves the stacking alternately of oriented dry fabrics and TP films and does not use semi-products in order to reduce material costs. This study was specifically directed towards optimizing the impregnation of continuous glass and carbon fibres reinforcing two TP amorphous matrices, the polyphenylsulfone (PPSU) and polyetherimide (PEI), to obtain semi-finished products employed for aeronautical structures. The impregnation quality of inter and intra-yarns is analyzed and validated by optical and scanning micrographic observations conducted with an optical and a Scanning Electron Microscopies (SEM), respectively. The study showed that besides the process parameters and porosity distribution in the core of warp yarns, the impregnation quality depends on the surface properties of constituents. Desizing treatment has been carried out to improve the wettability of fibres by the TP matrices.

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 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 score0.009
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

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)0.0000.000

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 head0.016
GPT teacher head0.242
Teacher spread0.226 · 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