Localization method for polyhedral-model-based Cutter/Workpiece Engagement calculation
Bibliographic record
Abstract
To improve the efficiency of polyhedral-model-based Cutter/Workpiece Engagement(CWE) calculation,an R-tree-based localization technique was proposed to reduce the number of facets which were considered in a given CWE calculation.The proposed method recorded the facets in a polyhedral model using an R-tree.According to the geometric features of the polyhedral model,an insertion algorithm for R-tree construction was presented to reduce sizes of the nodes in the R-tree and overlaps among them.The R-tree splitting axis and parameter were defined by geometrical relationship between node and toolpath so that the nodes in the R-tree were located along the toolpath and index efficiency could be improved.Once the R-tree was built,R-tree-based depth-first-search method was adopted to index the facets near the tool at any given position along the toolpath. Only these indexed facets were involved in the CWE calculation,and the efficiency of the calculation was improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".