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Record W3202847825 · doi:10.18280/rcma.310410

Performance of Athletic Prosthetic Feet Made of Various Composite Materials with PMMA Matrix: Numerical and Theoretical Study

2021· article· en· W3202847825 on OpenAlexvenueno aff
Hayder Kareem Talla, Jawad K. Oleiwi, Abdul Kareem F. Hassan

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2021
Typearticle
Languageen
FieldEngineering
TopicProsthetics and Rehabilitation Robotics
Canadian institutionsnot available
Fundersnot available
KeywordsComposite materialMaterials scienceGlass fiberComposite numberPolymethyl methacrylateDeformation (meteorology)Finite element methodFiberPolymerStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Prosthetic upgrades are specialized prosthetics that enable patients to participate in more demanding recreational activities, such as running. This study examines the use of prosthetic limbs, specifically the athletic prosthetic foot. The current research focused on the manufacturing and production properties of an samples athletic prosthetic Foot made from composite materials based on a polymethyl methacrylate resin (PMMA) reinforced with various fibers (UHMWPE, Perlon, Carbon fiber, and Glass fiber). The finite element method (ANSYS-19R) is used to build an athletic prosthetic model and apply boundary conditions to investigate the influence of deformation and stored energy on the performance of the sports prosthetic foot. Six laminates have been manufactured, and it has been discovered that adding a number of carbon fiber layers to UHMWPE has a better effect on deformation than adding a glass fiber 26% improvement. Furthermore, the findings show there is an improvement in performance when the number of classes was doubled, as the rate of improvement between the laminate to which carbon fibers were added was 31%, and between the laminate to which glass fibers were added by 32% under the same boundary conditions.

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.

How this classification was reachedexpand

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.192
Threshold uncertainty score0.823

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 science0.0000.000
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 head0.013
GPT teacher head0.238
Teacher spread0.225 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations18
Published2021
Admission routes1
Has abstractyes

Explore more

Same venueRevue des composites et des matériaux avancésSame topicProsthetics and Rehabilitation RoboticsFrench-language works237,207