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Record W4390233759 · doi:10.18280/rcma.330601

Enhancement of Mechanical Properties in Glass-Fiber Woven Reinforced Hybrid Composites for Aerospace Applications: An Empirical Investigation

2023· article· fr· W4390233759 on OpenAlexvenueno aff
Abeer Farouk Al-Attar, Hussein Alaa Jaber, Ammar Mousa Hasan

Bibliographic record

VenueRevue des composites et des matériaux avancés · 2023
Typearticle
Languagefr
FieldEngineering
TopicMechanical Engineering and Vibrations Research
Canadian institutionsnot available
Fundersnot available
KeywordsComposite materialMaterials scienceAerospaceGlass fiberFiberEngineeringAerospace engineering

Abstract

fetched live from OpenAlex

This experimental study aims to create innovative hybrid composites (HCs) for aerospace applications using the hand lay-up technique.Different volume fractions (Vf%) of distinctive natural (white clay (W) and red clay (R)) and synthetic (alumina (A) and woven glass fibers (E-GFW) reinforcing unsaturated polyester (UP).The mechanical and physical characterizations of these innovative HCs were estimated according to the American Society for Testing and Materials (ASTM) standards.The findings show that adding more white clay, red clay, and alumina to the HCs makes a big difference in improving their mechanical properties, such as Hardness Shore D (HSD), True Tensile Strength (Ttσ), Impact Strength (iσ), and Fracture Toughness (Kc).Analysis of variance (ANOVA) revealed noteworthy variations in the assessed parameters, emphasizing the potential suitability of these materials for aeronautical applications.This research contributes to the ongoing progress in material engineering, specifically in enhancing the mechanical resilience of composites utilized in the aerospace industry.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.288
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.115
GPT teacher head0.331
Teacher spread0.216 · 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 designSimulation or modeling
Domainnot available
GenreMethods

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

Citations1
Published2023
Admission routes1
Has abstractyes

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