Optimal portion control using variable cutter‐blade spacing in can‐filling
Bibliographic record
Abstract
Abstract In the present context, optimal portion control refers to the process of preparing can‐filling portions so that the deviation of the portion weight from a specified target weight is minimized. An approach has been developed for achieving this where a batch of objects is placed in a linearly overlapped optimal arrangement and then cut into portions using a series of parallel blades. The parameters of optimization are the arrangement order, orientation and degree of overlap of the objects. The approach has been demonstrated to produce impressive improvements in the application of fish canning. For this application, two approaches of optimal cutting are compared in the present paper. In one approach, the blade spacing is kept fixed and constant at a predetermined value. In the second approach, the blade spacing is varied for each portion after the objects are placed according to the optimal arrangement, where the target weight distribution is allowed to vary within a tolerance interval. The results presented in this paper indicate that the second approach produces a significantly higher percentage of acceptable portions than the first approach. What is presented are results from computer simulations, utilizing true data as measured from actual batches of fish. The paper demonstrates the potential benefit of the optimal portion control approach when applied in an industrial fish‐canning process. Copyright © 2001 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.000 | 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".