Bibliographic record
Abstract
It is generally recognized that resistance to cutting consists of two aspects, one reflecting the intrinsic strength of the material, and the other a frictional contribution. In practice, cutting usually involves a normal force and a sliding movement. This work aims to analyze the effects of friction on cutting resistance of materials in the presence of a normal force and sliding movement of a sharp object. International Standard ISO 13997 is used as a reference to evaluate the cut resistance of some selected protective materials under practical conditions in service. The resistance to cutting of a material in the presence of both a normal force and sliding movement of a sharp object is strongly controlled by friction between the blade and the cut material. An increase in the friction coefficient can enhance or reduce the cut resistance, depending on the thickness, and the microstructure of the material to be cut. Thus the total energy required to propagate a cut strongly depends on the friction coefficient and consists of two components: The lost energy dissipated by the squeezing force exerted by the cut material, due to its elasticity, on the blade sides, and the essential cutting energy at the edge of the blade. These energies have opposite effects on the cutting resistance of materials. An increase in the energy dissipated in gripping frictional force increases the cut resistance, whereas an increase in friction at the blade’s edge reduces the energy required to cut the 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 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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".