A Combined 3D Linear and Circular Interpolation Technique for Multi-Axis CNC Machining
Bibliographic record
Abstract
In multi-axis CNC machining of sculptured surfaces, a linear interpolation technique has been used to generate the command signals for positions along the straight line segments that connect each consecutive data point. Due to the rotational movements superimposed on the translational movements in multi-axis CNC machining, the actual cutter contact (CC) point moves along a space curve path, while the linear interpolation technique generates positions along the straight line path. The nonlinear curve segments deviate from the linearly interpolated line segments resulting in nonlinearity errors, which in turn, commonly cause difficulties to ensure high precision machining. An interpolator design technique for solving the nonlinearity errors problem in multi-axis CNC machining is presented. A combined 3D linear and circular interpolation principle is developed on the basis of the 3D linear and circular interpolation principles. The new designed interpolator is capable of driving the rotation movement pivot along a predesigned 3D curve path, so that the CC point motion trajectory is via a straight line connecting machining data points. Therefore, the proposed interpolator design technique on-line eliminates nonlinearity errors, and provides a solution to the nonlinearity errors problem for multi-axis CNC machining.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".