Bibliographic record
Abstract
The aluminum can industry in the United States and Canada manufactures over 100 billion cans per year. Two‐piece aluminum cans are commonly used to seal and deliver foodstuffs such as soft drinks, beer, pet food, and other perishable items. In order to ensure product safety and performance, the double seam between the can body and lid is a critical component of the package. Double‐seaming is a method by which the flange of the can body and the curl of the end are folded over together such that the final joint is composed of five metal thicknesses. There are a number of design challenges involved with the art of double seaming, especially with the push to lightweight. Although the requirements vary by product, the typical beer package must be able to hold pressures in excess of 90psi. In addition, in production, double seaming is a high‐speed operation with speeds as high as 3000 cans/minute on an 18‐spindle seamer. For this high volume, low cost industry, understanding and optimizing the seaming process can advance the industry as well as help prevent various manufacturing problems that produce a poor seal between the two pieces of the can.To aid in understanding the mechanics of the can parts during double‐seaming, a simulation procedure was developed and carried out on a 202 diameter beverage can and lid. Simulations were run with the explicit dynamics solver ABAQUS/Explicit using the continuum shell element technology available in the ABAQUS general purpose FEA program. The continuum shell is a shear‐deformable shell element with the topology of an eight node brick. The element’s formulation allows continuously varying, solution‐dependent shell thickness and through‐thickness pinching stress. One important advantage of using the continuum shell as opposed to a traditional shell element is that true contact interactions at the top and bottom surfaces of the can body and lid can be accurately modeled. With a conventional shell element, contact is performed at the shell mid‐surface or at an offset point representing where the top or bottom surface is expected to be.This paper discusses this new simulation technique and provides an example of its use.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".