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Record W3048448021 · doi:10.1177/0731684420949662

Characterization and numerical simulation of laminated glass fiber–polyester composites for a prosthetic running blade

2020· article· en· W3048448021 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.

Bibliographic record

VenueJournal of Reinforced Plastics and Composites · 2020
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsMaterials scienceComposite materialBendingPolyesterGlass fiberFiberBuckling

Abstract

fetched live from OpenAlex

The objective of this work was to explore different types of deformations (buckling, bending, and relaxation) on the properties of laminated composites based on polyester as the matrix and glass fiber in two forms: woven and chopped strand mat. The specimens were produced with the same thickness but with different number of ply. Also, a thin gelcoat based on clay particles was applied on the chopped strand mat samples to get a third series. The results showed that using the same thickness, the mechanical properties, especially in terms of bending and buckling, are influenced by the layers’ number. Furthermore, a sports application, which is the main objective of this work, is presented as an applied investigation for a leg prosthesis. Three different running blades “Flex-foot Cheetah” were manufactured to be experimentally and numerically (ANSYS ACP software) characterized to simulate real conditions. The results showed a good agreement between the experimental and numerical values in terms of total displacement, which is around 50 mm, the produced blade has been tested in quasi-static and dynamic compression, and results showed that the relaxation behavior depends on the structure design and the used materials.

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 score0.431
Threshold uncertainty score0.668

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 head0.012
GPT teacher head0.235
Teacher spread0.222 · 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