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Record W2472446412 · doi:10.1177/0021998316654748

Modelling of mechanical properties of randomly oriented strand thermoplastic composites

2016· article· en· W2472446412 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

VenueJournal of Composite Materials · 2016
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsNational Research Council CanadaMcGill University
FundersNational Research Council CanadaPratt and Whitney CanadaMcGill University
KeywordsMaterials scienceComposite materialFormabilityPeekModulusPolyether ether ketoneFinite element methodThermoplasticAerospaceFracture (geology)Structural engineeringPolymer

Abstract

fetched live from OpenAlex

There is an emerging interest in the aerospace industry to manufacture components with intricate geometries using discontinuous-fibre carbon/polyether-ether-ketone moulding systems (obtained by cutting unidirectional tape into strands). Great formability and high modulus can be achieved with this type of composites, but the high variability of measured properties can have a detrimental effect on the design allowables. When it comes to prediction of mechanical properties, it is important to capture the average strength and modulus as well as their statistical variability. This article proposes a stochastic finite element technique that uses the concept of randomly oriented strands to model variability, and the application of Hashin’s failure criteria and fracture energies to estimate strength. Overall, the model matches the trends observed during experiments and shows that strength of randomly oriented strand composites is significantly lower than that of continuous-fibre laminates due to the ‘weakest-link’ principle.

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)0.001
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 score0.099
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.025
GPT teacher head0.211
Teacher spread0.186 · 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