Packaging optimization using the dynamic vector fields method
Bibliographic record
Abstract
Summary In this paper, a novel packaging optimization method for convex objects is presented. This method solves the packaging optimization problem through dynamic simulation of object positions and rotations over time. Object positions and orientations are determined by dynamic vector fields, which accelerate objects according to optimization objectives or physical effects between objects or their environment. Using these vector fields, any number of objectives or effects can be accounted for, and this scalability allows the method to potentially be employed to solve a wide variety of engineering packaging optimization problems. The current implementation, as presented in this paper, represents the foundation of the method that future improvements will build upon and is currently limited to the analysis of convex objects. Three basic vector fields are presented to solve packing density maximization problems: the first maximizes packing density, the second prevents collisions between objects, and the third optimally orients objects relative to each other. Collisions between objects are relaxed in this method, allowing objects to pass through each other, which provides the potential for reduced initial condition dependence and has shown promising results thus far. Several test problems are presented and solved, demonstrating the method and its ability to generate optimal solutions.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".