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Record W2041563213 · doi:10.1177/0892705702015002449

Mechanics of Damage and Degradation in Random Short Glass Fiber Reinforced Composites

2002· article· en· W2041563213 on OpenAlex
Marie‐Laure Dano, Guy Gendron, Hicham Mir

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 Thermoplastic Composite Materials · 2002
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsUniversité Laval
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMaterials scienceComposite materialDamage mechanicsIsotropyUltimate tensile strengthAnisotropyWork (physics)Degradation (telecommunications)Tensile testingGlass fiberTensor (intrinsic definition)FiberStructural engineeringFinite element methodComputer scienceThermodynamics

Abstract

fetched live from OpenAlex

The present study is part of an ongoing research work on damage of random short fiber reinforced composites. First, the tensile behavior of the material was studied. In its initial state, the material is planarly isotropic elastic. As the load increases, damage induces an anisotropic degradation of the material properties. Then, the degradation mechanisms were studied from microscopic observations of polished specimens. Finally, a theoretical model based on damage mechanics is proposed to predict the mechanical behavior of the material. Damage variables are used to evaluate the change of the compliance tensor. The evolution laws of the damage variables are established within a thermodynamic framework using the associated thermodynamic forces. Correlation with the tensile test results is good. However, additional experiments have to be carried out to fully validate the proposed model.

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 score0.043
Threshold uncertainty score0.778

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 studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.014
GPT teacher head0.215
Teacher spread0.201 · 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