Computation of the maximum torque of a cap liner using a power‐law friction and finite element analysis
Bibliographic record
Abstract
Abstract In the ‘filled closed’ containers, the cap removal torque and the sealing of the contents are two crucial criteria in the closing quality, and the consumer perceives these two parameters as constituting a guarantee of packaging integrity. This work looks at the experimental study and the finite element analysis of the maximum torque of a metal cap with a liner over a glass bottle. For studying parameters influencing the cap removal torque (twist‐off torque), several experiments and simulations were conducted in order to evaluate the maximum torque of a loose crown cap. A test bench was built to measure the torque required to slide a cap liner on the top of a glass bottle, and the result of the experiment is compared with the predicted torque given by an axisymmetric finite element (FE) analysis. Since the behaviour of the cap liner is hyperelastic, compression and friction tests were conducted to evaluate the elastic properties and the non‐linear static friction behaviour of the elastomer seal. Contact regions, material non‐linear elasticity for the liner and large displacement options are included in the FE model in order to describe the evolution of the contact area and the distribution of contact pressure as a function of the applied force. The predicted maximum torque is then calculated by a numerical integration scheme over the contact surface, using an experimental coefficient of static friction as a function of the normal pressure. The predicted torque shows good agreement with that measured through experiments, thereby making it possible to understand the influence of the liner on the cap removal torque of a glass bottle. Copyright © 2010 John Wiley & Sons, Ltd.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".