Triangle Mesh Based In-Process Workpiece Update for General Milling Processes
Bibliographic record
Abstract
A new methodology for modeling and updating the in-process workpiece geometry in milling is presented in this paper. The methodology is developed for general milling processes, in which the cutter can be any shape and follow any tool path trajectory even with self-intersections. And the in-process workpiece is updated with retained sharp features. The associated procedure starts by modeling both the cutter and the workpiece as closed manifold triangle meshes. The mesh model of the cutter swept volume is then generated from repeatedly sampled mesh vertices of the cutter along its trajectory using the ball-pivoting algorithm. The workpiece is updated by a subtraction Boolean operation between the workpiece and the cutter swept volume. An octree space partitioning algorithm is adopted in order to efficiently obtain the exact triangle-to-triangle intersection points. As the last step, a filling operation is performed around the intersection points to establish the closed manifold updated workpiece geometry. Several case studies have been performed to demonstrate the effectiveness of the proposed methodology.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 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".