Particle Dynamics Modeling of the Creping Process in Tissue Making
Bibliographic record
Abstract
The manufacturing of low-density paper such as tissue and towel typically involves a key operation called creping. In this process, the wet web is continuously pressed onto the hot surface of a rotating cylinder sprayed with adhesive chemicals, dried in place, and then scraped off by a doctor blade. The scraping process produces periodic microfolds in the web, which enhance the bulk, softness, and absorbency of the final tissue products. Various parameters affect the creping process and finding the optimal combination is currently limited to costly full-scale experiments. In this paper, we apply a one-dimensional (1D) particle dynamics model to systematically study creping. The web is modeled as a series of discrete particles connected by viscoelastic elements. A mixed-mode discrete cohesive zone model (CZM) is embedded to describe the failure of the adhesive layer. Self-contact of the web is incorporated in the model using a penalty method. Our simulation results delineate three typical stages during the formation of a microfold: interfacial delamination, web buckling, and post-buckling deformation. The effects of key control parameters on creping are then studied. The creping angle and the web thickness are found to have the highest impact on creping. An analytical solution for the maximum creping force applied by the blade is derived and is found to be consistent with the simulation. The proposed model is shown to be able to capture the mechanism of crepe formation in the creping process and may provide useful insights into the manufacturing of tissue paper.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".