On the B-spline interpolated tool trajectories for five-axis sculptured surface machining
Bibliographic record
Abstract
B-spline interpolation scheme is now available on modern five-axis Computer Numerical Control (CNC) machine tools. With this newly implemented interpolation scheme, a cutting tool can be directly commanded to trace B-spline trajectories, which approximate ideal 3D curved trajectories, in sculptured surface machining. The approximation of ideal tool trajectories by B-spline interpolated tool trajectories inevitably leads to machining errors, referred to as the geometry-based errors in the present work. It is essential to ensure synchronisation of the movements of the three translational and two rotational joints of a five-axis machine tool to reduce the geometry-based errors. This paper presents an effective method to achieve synchronisation of the machine joint movements. It first fits a 3D B-spline for the three translational joints and then uses a knot inheriting procedure to fit a 2D B-spline for the two rotational joints. Evaluation of the presented method was made through the machining of a typical bi-cubic Bezier surface on a five-axis machine tool capable of performing non-uniform B-spline interpolation. It was found that the resulting geometry-based errors, which were varying along the given isoparametric tool paths, were able to be maintained below 25m.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 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".