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Record W3132727610 · doi:10.1142/s1758825121500034

Finite Element Study of Mixed Fracture: Velocity-Dependent Insertion Of Pointed Blades Into Soft Material

2021· article· en· W3132727610 on OpenAlexaff
Marwa Gzaiel, Ennouri Triki, Abdelwahed Barkaoui, Moez Chafra

Bibliographic record

VenueInternational Journal of Applied Mechanics · 2021
Typearticle
Languageen
FieldEngineering
TopicMechanical stress and fatigue analysis
Canadian institutionsCollège Communautaire du Nouveau-BrunswickNew Brunswick Community College
Fundersnot available
KeywordsMaterials scienceFinite element methodViscoelasticityTension (geology)ElastomerFracture toughnessComposite materialFracture (geology)Stress (linguistics)Shear (geology)Fracture mechanicsNatural rubberMechanicsDeformation (meteorology)Structural engineeringCompression (physics)EngineeringPhysics

Abstract

fetched live from OpenAlex

This study concerns the effect of applied velocity on the energy state and stress state related to the puncture-cutting of soft material. A finite element modeling (FEM) of combined puncture and cutting of neoprene rubber by a pointed blade was established at 17 velocities (from 10[Formula: see text]mm/min to 1500[Formula: see text]mm/min). The proposed FEM takes into consideration, the nonlinear material behavior of the elastomeric substrate. First, puncture-cutting tests are conducted and the evolution of puncture-cutting force with increased velocity is investigated. Second, the commonly used puncture-cutting energy criterion, including the fracture toughness of material and the friction energy occurring between the material and the pointed blade, are summarized and analyzed. Finally, an analysis of the stress state in the fracture process zone is proposed. Results show that the puncture-cutting force increases significantly with increasing insertion velocity. A low velocity of the pointed blade is dominated by a uniaxial tension with a constant energy, while a medium velocity causes a dominant biaxial tension with increasing energy, which may be the source of the viscoelastic deformation involved around the crack tip. However, an increase of the velocity increases the shear stress up to a maximum value and, therefore, shows a toughening of material.

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 score0.320
Threshold uncertainty score0.509

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 head0.010
GPT teacher head0.236
Teacher spread0.226 · 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

Citations8
Published2021
Admission routes1
Has abstractyes

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