The tensile mechanics of creped fiber networks: Effects of interfacial-delamination, buckling, and damage
Bibliographic record
Abstract
Paper products, like tissue paper, are composed of bonded wood fiber networks. Dry creping is an industrial process used in tissue manufacturing. In this process, a wet paper sheet (web) is adhered to a high-speed metal dryer (substrate). The dried sheet is then scraped off against a stationary metal blade, leading to web-substrate debonding, sheet folding, and damage caused by the rupture of interfiber bonds. This process creates a microfolded structure, leading to a nonlinear tensile response and high failure strain, while sheet-damage results in sheet de-densification (through thickness explosion). Based on the visualized creped structures, creped sheets are classified as shaped-bulk (folding-dominated) or explosive-bulk (damage-dominated). While factors affecting sheet-folding have been studied extensively, the effects of sheet-damage on structural and tensile properties have not been previously studied. Using a Discrete Element Method (DEM) to model low grammage fiber networks, we simulate creping with a bilinear elastoplastic fiber model. We demonstrate that altering sheet–substrate bond (adhesive) properties relative to interfiber bonds shifts creping from shaped-bulk to explosive-bulk. Signatures of the above two creping modes are identified. Shaped-bulk sheets exhibit fewer interfiber bond ruptures, a higher degree-of-folding (waviness), and a less through-thickness explosion, while explosive-bulk sheets show the opposite traits. During tensile deformation, bending dominates initially, followed by an increased axial deformation near failure as unfolding occurs. The transition from shaped-bulk to explosive-bulk creping shows an initial increase in stiffness followed by a decline, and a gradual then rapid, decrease in tensile strength.
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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 | 0.000 |
| 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 | 0.000 | 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".