Finite Element Study of Mixed Fracture: Velocity-Dependent Insertion Of Pointed Blades Into Soft Material
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".