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Record W2873031260 · doi:10.5539/jmsr.v7n3p12

Experimentally and analyzed property of carbon fiber reinforced thermoplastic and thermoset plates

2018· article· en· W2873031260 on OpenAlexvenueno aff
Mitsuhiro Okayasu, Yuki Tsuchiya, Hiroaki Arai

Bibliographic record

VenueJournal of Materials Science Research · 2018
Typearticle
Languageen
FieldMaterials Science
TopicNatural Fiber Reinforced Composites
Canadian institutionsnot available
Fundersnot available
KeywordsMaterials scienceUltimate tensile strengthComposite materialEpoxyThermosetting polymerPeekThermoplasticPolymer

Abstract

fetched live from OpenAlex

The tensile and fatigue properties of long unidirectional (UD) and crossply (CR) carbon fiber reinforced plastics (CFRPs) were investigated. The CFRPs in this study were fabricated from 60% CF and various resins: epoxy, polyamide (PA6), polyphenylene sulfide (PPS), and polyether ether ketone (PEEK). The ultimate tensile strength sUTS of Epoxy-CFRP was found to be about twice that of PEEK-CFRP. Relatively high tensile strengths were found for PPS- and PA6-CFRP in the thermoset resin group, although these were still only about 85% of the strength of epoxy-CFRP. The tensile and fatigue strengths of the CR-CFRPs were less than half those of the UD-CFRPs, even though high ductilities were found for the CR-CFRPs. These high ductilities can be attributed to the crosslinking fiber effect and the low proportion of CFs in the loading direction. The sUTS values of CFRPs depend not only on the tensile strengths s and volume fractions V of CF and resin (i.e., through the conventional compound law sUTS = sfiberVfiber + sresinVresin), but also on several material properties, including the wettability of the CF by the resin. On the basis of the material properties, the ultimate tensile strengths of various UD- and CR-CFRPs were well estimated numerically through a statistical analysis, which afforded better estimates than those obtained from the compound law.

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)0.005
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.005
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.003
Scholarly communication0.0000.001
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 head0.040
GPT teacher head0.345
Teacher spread0.305 · 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.

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
Published2018
Admission routes1
Has abstractyes

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