Optimal layout and path planning for flame cutting of sheet metals
Bibliographic record
Abstract
This paper presents a two-stage methodology for the optimisation of layout and path planning that can be used in flame cutting of sheet metals. The main objective of this paper is to minimise the total travel distance of the flame gun that will not only reduce the cutting time but also the heat effect. The first stage is to use a heuristic to cluster a set of small rectangular items (called workpieces) into one or more large rectangular objects (called blocks) and then best fit these blocks into a given stock. By allowing clustered small items to be cut along common borderlines, the travel distance within blocks is minimised. The second stage is to use genetic algorithms (GAs) to determine an optimal path in consideration of multiple start points for each block. The proposed path planning method provides an advantage by minimising the travel distance between blocks. The combination of the two solutions leads to minimisation of the total travel distance. To demonstrate the effectiveness of the proposed method, a number of cases are studied with results showing a 20% average reduction in the total travel distance in comparison to conventional methods.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".