A New Approach to Planning Plungers Paths for Efficient 2½-Axis Computer Numerically Controlled Plunge Milling of Complex Pockets With Islands
Bibliographic record
Abstract
Plunge milling is an effective roughing operation, especially in pockets roughing, because it can efficiently remove a large amount of stock material without high manufacturing costs. However, plunge milling of complex pockets with islands, whose boundaries could be designed with free-form curves, is quite challenging for multiple plungers have to be used including small plungers to cut necks between islands and their plungers paths are expected to have fewer times of plunging and shorter travel to achieve efficient machining. Unfortunately, little research on this topic was carried out in the past, and the challenge has not been addressed yet. In this research, a new approach is proposed to generate plunger paths for efficient plunge milling of the complex pockets. Its main features include (1) packing plunger circles at a minimum number of locations inside the pocket for fewer times of plunging, (2) placing plunger circles to cover the areas enclosed by the afore-packed circles to clear out the interior pocket material, and (3) planning the shortest paths to connect plunger locations for less traveling time. The advantages of this new approach over the overlapped circles filling (OCfill) and the Catia methods are demonstrated with two examples, and it can be directly used for pocket plunge milling in industry.
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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 | 0.000 | 0.000 |
| 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) | 0.003 | 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".