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Analysis of Stress Concentration Factor for Tensile Characteristics of Syntactic Foam Using Finite Element Method

2017· article· en· W2607389107 on OpenAlexvenueno aff
Zulzamri Salleh, Md Mainul Islam, Jayantha Epaarachchi‎

Bibliographic record

VenueJournal of Research Updates in Polymer Science · 2017
Typearticle
Languageen
FieldEngineering
TopicCellular and Composite Structures
Canadian institutionsnot available
FundersUniversiti Kuala LumpurMajlis Amanah Rakyat
KeywordsSyntactic foamMaterials scienceUltimate tensile strengthModulusComposite materialGlass microsphereYoung's modulusFinite element methodStress (linguistics)Structural engineeringMicrosphere

Abstract

fetched live from OpenAlex

This paper presented the stress concentration factor (SCF) around the half circular edge of tensile specimens made of syntactic foam using finite element software Strand7 software. The study is a preliminary effort, which investigates the effect of variations of crack geometry on the stress concentration factor on a tensile specimen subjected to a constant, uniform, uniaxial tensile load. The material property is graded for varying Young’s Modulus and Poisson’s Ratio with different composition of glass microballoons. Finally, a uniform pressure is applied at the top and the model is constrained with symmetric boundary conditions at the left and bottom. As the result, these numerical results for both SCF experimental and simulation model are compared to those obtained from analytic fracture mechanics procedures and are found to be varied. In addition, the SCF is sensitive to the modulus of elasticity, particularly for lower composition weight percentage (wt.%), while it is also varied with the different weight percentage (wt.%) of glass microballoons, which is led by 2 wt.% specimen.

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 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 score0.264
Threshold uncertainty score0.233

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
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 head0.050
GPT teacher head0.402
Teacher spread0.352 · 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

Citations0
Published2017
Admission routes1
Has abstractyes

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