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Record W3092787903 · doi:10.1002/app.50009

Effects of hygrothermal aging on the thermomechanical properties of a carbon fiber reinforced epoxy sheet molding compound: An experimental research

2020· article· en· W3092787903 on OpenAlexaff
Kepa Zulueta, Aizeti Burgoa, Iñaki Martínez

Bibliographic record

VenueJournal of Applied Polymer Science · 2020
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsASTER
Fundersnot available
KeywordsEpoxyMaterials scienceComposite materialMolding (decorative)Dynamic mechanical analysisSheet moulding compoundComposite numberUltimate tensile strengthRelative humidityGlass fiberFiberPolymer

Abstract

fetched live from OpenAlex

Abstract Carbon fiber sheet molding compounds (C‐SMCs) are discontinuous fiber reinforced composite materials. Among them, epoxy‐based C‐SMCs are becoming relevant materials due to their high thermomechanical performance and better formability than continuous fiber reinforced composites. The thermomechanical performance of epoxy resins and epoxy based continuous carbon fiber composites have shown to be influenced by hygrothermal aging. In this work, this influence is studied for an epoxy‐based C‐SMC. Epoxy‐based C‐SMC samples were hygrothermally aged by means of accelerated conditioning, exposing them to 65% relative humidity, and 80°C in a climatic chamber. The equilibrium moisture content, as well as the moisture diffusion coefficient has been determined. The thermomechanical properties of epoxy C‐SMC have been analyzed by dynamic mechanical analysis, tensile, 3‐point bending, and short beam tests in dry and aged samples. The results showed that epoxy C‐SMC is affected by hygrothermal aging in the cases of moisture intake and its effects on T g value, but interestingly, the hygrothermal aging did not generate any degradation effects in the mechanical response of epoxy C‐SMC.

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)0.001
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 score0.017
Threshold uncertainty score0.387

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
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 head0.042
GPT teacher head0.279
Teacher spread0.237 · 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

Citations25
Published2020
Admission routes1
Has abstractyes

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