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Record W2145126245 · doi:10.5897/jmer.9000022

Fractography of compression failed carbon fiber reinforced plastic composite laminates

2010· article· en· W2145126245 on OpenAlexvenueno aff
M.S. Vinod, Sunil B. J, Vinay Nayaka, Raghavendra Shenoy, M. S. Murali, A. Nafidi

Bibliographic record

VenueMechanical Engineering Research · 2010
Typearticle
Languageen
FieldEngineering
TopicMechanical Behavior of Composites
Canadian institutionsnot available
Fundersnot available
KeywordsFractographyMaterials scienceComposite materialFracture (geology)Composite numberCompression (physics)Composite laminatesFibre-reinforced plasticScanning electron microscopeStructural engineeringEngineering

Abstract

fetched live from OpenAlex

Widespread usage of composites in the advanced technologies, has led to the study of the failure modes by which these composites fail, as identification and hence the interpretation of these characteristic fracture features is necessary to provide valuable information in understanding of the failure behavior of composites. Hence, in this paper, our effort has been in the study of the fractographic features in the carbon fiber reinforced plastic (CFRP) composite laminates under compression loading. Microscopic study with the aid of scanning electron microscope (SEM) has been performed on failed composite fracture surfaces to identify the principal features due to compression loading. This research has led to the knowledge of the nature and origin of fracture, as well as understanding of how the fracture occurs, with an insight into the various characteristic features and its effect of these on the failure modes with the definition of overall crack propagation direction in case of compression loaded CFRP laminates. Also, a model explaining the damage zone leading to kink band formation has been discussed. Key words: CFRP, compression loading, fractography, SEM, damage zone model, kinking.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.017
GPT teacher head0.274
Teacher spread0.257 · 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 source (direct Gemma or distilled Codex), 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

Citations16
Published2010
Admission routes1
Has abstractyes

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