A New Approach to G1 Biarc Approximations for Making Smooth, Accurate, and Non-Gouged Profile Features Using CNC Contouring
Bibliographic record
Abstract
Extensive research on G1 biarcs fitting to free-form curves (i.e., Bezier, B-spline, and NURBS curves) has been conducted in the past decades for various purposes, including CNC contouring to make smooth, accurate profile features such as pockets, islands, and sides. However, all the proposed approaches only focused on the approximation errors and the biarc number, not on the radius of the individual fitting arc; so it could be smaller than the cutting tool, which would cause gouging during machining. This work, based on the tool radius pre-determined by the minimum size of the concavities of the design profile, proposes a new approach to approximating the profile with a G1 biarc curve in order to make smooth, accurate, and non-gouged profile features using CNC contouring. The significant new contribution of this work is a new mechanism that ensures all the concave arcs of the fitting curve are larger than the pre-determined tool and the fitting errors meet the specified tolerance. This approach can promote the use of G1 biarc tool paths in the manufacturing industry to make high precision profile features.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".