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Record W4306795346 · doi:10.21203/rs.3.rs-2166524/v1

Experimental study and numerical simulation of cutting and tearing of silicone rubber using Extended Finite Element Method (XFEM)

2022· preprint· en· W4306795346 on OpenAlexaff
Marwa Gzaiel, Ennouri Triki, Abdelwahed Barkaoui, Moez Chafra

Bibliographic record

VenueResearch Square · 2022
Typepreprint
Languageen
FieldEngineering
TopicElasticity and Material Modeling
Canadian institutionsCégep Marie-Victorin
Fundersnot available
KeywordsSilicone rubberTearingMaterials scienceDissipative systemFinite element methodDeformation (meteorology)DissipationFracture mechanicsNatural rubberStrain energy release rateShear (geology)ViscoelasticityComposite materialNonlinear systemStructural engineeringStrain rateFracture (geology)EngineeringPhysics

Abstract

fetched live from OpenAlex

Abstract Failure of soft materials is a fundamental challenge due to the strongly nonlinear and dissipative deformation involved. An experimental and extended finite element study of dynamic crack in silicone rubber are investigated. Hence, material preparation procedures, details of sample as well as testing apparatus which have been used for cutting, pure shear tests are presented. First, the rate of energy restitution and an instantaneous propagation speed were achieved. The crack propagation speed / energy release rate relationships are given for the different strain rates. second, an analysis of the mechanical fields and stress state in the fracture process zone is proposed. Finally, cutting force evolution according to stretches is established. Then, an energy-based approach was introduced. Results show that the cutting force and the total cutting energy decreases significantly with increasing deformation rate.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.068
Threshold uncertainty score0.795

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0000.001
Research integrity0.0000.001
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 head0.111
GPT teacher head0.439
Teacher spread0.328 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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