A New High Precision Fitting Approach for NURBS Tool Paths Generation
Bibliographic record
Abstract
In new models of CNC machines, NURBS interpolation function can process tool paths represented in NURBS form (called NURBS tool paths) so that cutters can conduct NURBS motions, which can machine sculptured surfaces with high surface accuracy and finish. However, most CAM systems could not convert the tool paths with discrete cutter locations into NURBS tool paths, and the conventional data fitting methods can not calculate a NURBS curve to represent the given cutter locations in high precision. In this work, a new high precision fitting approach is proposed for generating NURBS tool paths. The main contribution of this work is to propose NURBS tool paths and fit the tool paths through the cutter locations more precisely with fewer control points. Since NURBS tool paths can make accurate and smooth sculpture surfaces, this approach can promote the usage of NURBS tool paths in manufacturing industry.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".