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Record W4293108704 · doi:10.1080/20550340.2022.2057137

Repair of thermoplastic composites: an overview

2022· article· en· W4293108704 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 · 2022
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsÉcole de Technologie SupérieureMcGill UniversityNational Research Council Canada
FundersNational Research Council CanadaCentre de Recherche sur les Systèmes Polymères et Composites à Haute Performance
KeywordsThermosetting polymerAerospaceComposite materialThermoplastic compositesMaterials scienceThermoplasticComposite numberCertificationWeldingAdvanced composite materialsComputer scienceEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

An extensive review of literature is conducted to present the evolution of the field of repair
\nof thermoplastic composites (TPC’s) from when it was first mentioned in 1980. The TPC
\nmaterials used today in aerospace structures are introduced along with the existing chal-
\nlenges to repair TPC structures. The three most promising fusion bonding techniques to
\naddress these challenges (i.e. induction, resistance, and ultrasonic welding) are explained.
\nThe certification authorities have extensive knowledge and data for repair of thermoset poly-
\nmer matrix composite structures. However, such level of knowledge is highly limited for TPC
\nstructures. A lack of robust processes and the overall lack of data on TPC’s when compared
\nto their thermoset counterparts are challenges that need to be addressed to implement TPC
\nmaterials in aircraft structures.

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 score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0010.001
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 head0.021
GPT teacher head0.265
Teacher spread0.245 · 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