A New Principle of CNC Tool Path Planning for Three-Axis Sculptured Part Machining—A Steepest-Ascending Tool Path
Bibliographic record
Abstract
Three-axis CNC milling is often used to machine sculptured parts. Due to the complex surface shape of these parts, well-planned tool paths can significantly increase the machining efficiency. In this work a new principle of CNC tool path planning for 3-axis sculptured surface machining is proposed. Generic formula to calculate the steepest tangent direction of a sculptured surface is derived, and the algorithm of the steepest-ascending tool path generation is introduced. A single steepest-ascending tool path has been verified to be more efficient than a single tool path of any other type. The relationship between machining efficiency and three key variables, tool feed direction, cutter shape, and surface shape, is revealed. The newly introduced principle is used in planning tool paths of a sculptured surface to demonstrate the advantages of the steepest-ascending tool paths. This new tool path scheme is further integrated into the more advanced steepest-directed and iso-cusped (SDIC) tool path generation technique. Applications of the new tool path principle and the SDIC tool paths to the machining of sculptured parts are demonstrated.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".