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Record W2593103540 · doi:10.1002/app.44711

Model development for work of fracture of hybrid composites

2017· article· en· W2593103540 on OpenAlexaff
Hamideh Hajiha, Mohini Sain

Bibliographic record

VenueJournal of Applied Polymer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsComposite materialMaterials scienceFracture (geology)FiberWork (physics)Fiber pull-outComposite numberMatrix (chemical analysis)Composite laminatesMechanical engineering

Abstract

fetched live from OpenAlex

ABSTRACT The theoretical modeling for work of fracture of hybrid composites was investigated. Current available models for the work of fracture mostly consider single fiber composites and are not supported by experimental evidence. This is due to the complex nature of work of fracture, and presence of far too many variables for the experimental validation such as interaction of fibers with matrix and with each other. In this work, a model was developed for hybrid composites based on the modification of rule of mixtures, considering pull‐out, fracture, debonding, and stress redistribution for fibers along fracture for matrix. Later, the model was experimentally evaluated by GF‐hemp‐PP hybrid composites, and showed a better agreement in comparison to a prevalent model for work of fracture. Aside from matrix failure, the dominant failure mechanisms were fiber stress redistribution for long fiber composites and fiber pull out for short fiber composites. © 2017 Wiley Periodicals, Inc. J. Appl. Polym. Sci. 2017 , 134 , 44711.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.007
Threshold uncertainty score0.348

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 science0.0010.000
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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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 designBench or experimental
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

Citations4
Published2017
Admission routes1
Has abstractyes

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