CNC Machining Path Planning Optimization for Circular Hole Patterns via a Hybrid Ant Colony Optimization Approach
Bibliographic record
Abstract
This paper presents a path-planning optimization study for a Computer Numerical Control (CNC) machining center tasked with machining jobs involving a large number of holes to drill that are mostly arranged in concentric circular patterns. Benefits of this research may contribute to shortening the machining time in certain components used in heat exchangers, boilers, condensers, trammel screens and food separators. Optimization of tool travel distance and machining cost are typically overlooked aspects when generating tool paths and CNC codes from commercially available CAD software packages. Tool path travel distance minimization can be modelled Travelling Salesman Problem (TSP). Optimization algorithms have been heavily applied in the literature to the TSP with varying levels of success. Ant Colony Optimization (ACO) is one of the most prominent approaches that mimics the natural behavior of ant colonies. The research in this paper proposes a hybrid ACO that has a biasing mechanic designed to take advantage of the geometric hole-pattern arrangement, as well as a local search. Simulation examples show the proposed approach exhibiting superior performance compared to the classic ACO approach, a genetic algorithm (GA) approach, as well as the simple spiral path generated via commercial CAD software. The proposed approach is then applied to the drilling path planning of a two-thousand-hole food-industry separator plate.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".