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Record W4399802964 · doi:10.5267/j.esm.2024.3.003

Effects of thermal conditions on fatigue behaviour of laminated glass/epoxy plates under tension-tension cycle

2024· article· en· W4399802964 on OpenAlexvenueno aff
Madhusmita Biswal, Priyadarshi Das, S. K. Sahu

Bibliographic record

VenueEngineering Solid Mechanics · 2024
Typearticle
Languageen
FieldEngineering
TopicStructural Analysis of Composite Materials
Canadian institutionsnot available
Fundersnot available
KeywordsEpoxyTension (geology)Materials scienceComposite materialThermalLow-cycle fatigueThermal fatigueLaminated glassStructural engineeringCompression (physics)Engineering

Abstract

fetched live from OpenAlex

The fatigue behavior study on laminated glass/epoxy composite plates at elevated temperature is attempted in the present research work. INSTRON 8862 servo-electric universal testing machine with hydraulic suspension is utilized to perform low cyclic tension-tension fatigue tests at 0.5Hz and 0.7Hz frequencies. Bluehill universal software compatible with INSTRON 8862 is employed to obtain the relationship of fatigue stress upon cycles to failure (S – N) for each specimen. The parametric investigation is done for the loading frequency, lamination sequence and number of layers to understand their effects on fatigue behavior of composite laminated plates under ambient and thermal environment. The elicited results lead to the conclusion that above parameters greatly influenced the fatigue behavior of composite laminated plates under thermal loading. The rising temperature has significant adverse effects on fatigue life. The present research is beneficial for the analysis and design of laminated composite plate or plate-like structures in the domain of fatigue analysis.

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)0.001
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 score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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)0.0020.000

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 head0.006
GPT teacher head0.223
Teacher spread0.217 · 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

Citations1
Published2024
Admission routes1
Has abstractyes

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