THE APPLICATION OF PORTION CONTROL OPTIMIZATION IN AN AUTOMATED CAN‐FILLING PROCESS
Bibliographic record
Abstract
ABSTRACT The main objective of portion control is to ensure that desired portions, usually specified by weight, are placed in the packages. In can‐filling in particular, an optimal goal would be to minimize underfilling and overfilling. This paper develops an advanced packaging process for automated can‐filling of fish, which achieves this goal. The overall automated system uses an innovative technique of optimal overlapping and cutting of fish. First, a batch of fish are overlapped in a linear orientation where the ordering sequence, the head orientation, and the degree of overlap between fish are the variables of optimization. The optimization is carried out with the objective of minimizing the absolute total weight of underfill and overfill of the produced cans. The optimal portioning method should possess a computational speed that is consistent with the process speed and the filling accuracy requirements. Several models of optimization have been developed. This paper follows a model development procedure that realizes a feasible and practical model. A numerical example that uses real data on a batch of salmon is presented to illustrate the approach and to demonstrate its feasibility in achieving both optimization objective and the processing speed. A comparison of several optimization models that have been developed is given, with respect to the computational speed and the filling accuracy. Results show that the optimal portioning method is able to achieve high production rates and improved filling accuracy in can‐filling process of salmon.
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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 | 0.000 |
| Scholarly communication | 0.001 | 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".