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Record W2989514674 · doi:10.18280/rcma.290308

Experimental Investigation of Tensile Properties in a Glass/Epoxy Sample Manufactured by Vacuum Infusion, Vacuum Bag and Hand Layup Process

2019· article· en· W2989514674 on OpenAlexvenueno aff
Amin Abbasi Talabari, Mohammad Hossein Alaei, Hamid Reza Shalian

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2019
Typearticle
Languageen
FieldEngineering
TopicAdditive Manufacturing and 3D Printing Technologies
Canadian institutionsnot available
FundersMalek-Ashtar University of Technology
KeywordsEpoxyMaterials scienceComposite materialUltimate tensile strengthSample (material)Process (computing)Computer scienceChromatographyChemistry

Abstract

fetched live from OpenAlex

In order to achieve a high-quality composite, a proper manufacturing technique should be used. Glass Fiber Reinforced Plastic (GFRP) structures are commonly manufactured using hand layup, vacuum bag and vacuum infusion process (VIP) which are cost effective techniques. This paper compares the tensile strength, modulus, inter-laminar bonding and surface macroscopy of GFRP composites made by hand layup, vacuum bag and VIP process. For this reason, at first GFRP sample were manufactured using these three methods and then tested for tensile properties. In the next step, these samples were modeled numerically and compared. The results show that the strength of the sample made by VIP process is 20 % higher than the hand layup and 11 % higher than vacuum bag sample. Also, the modulus of the sample achieved using VIP is 21 % higher that hand layup and 15% higher than vacuum bag technique. By comparing the failure mechanism of the samples, it was observed that the inter-laminar bonding is highest in VIP and then vacuum bag and lastly hand layup due to a smaller number of voids. Lastly the numerical results showed to have a good agreement with the experimental results.

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 score0.012
Threshold uncertainty score0.959

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 science0.0000.000
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 head0.028
GPT teacher head0.233
Teacher spread0.205 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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