An Automated and Accurate CNC Programming Approach to Five-Axis Flute Grinding of Cylindrical End-Mills Using the Direct Method
Bibliographic record
Abstract
In solid carbide end-mills, the flutes significantly affect the tool's cutting performance and life, and the core radius mainly affects the tool's rigidity. The current CNC programming techniques can correctly determine the orientation of the wheel so that it grinds the rake face with the specified rake angle; however, it cannot accurately determine the wheel location for the direct method and, consequently, the desired core radius is not guaranteed. To address this problem, a new CNC programming approach is proposed to accurately calculate the wheel orientation and location (WOL) in 5-axis grinding of the cylindrical end-mill flutes. In this work, a new concept of 5-axis CNC grinding—effective grinding edge (EGE)—is first proposed to represent the instantaneous grinding edge of the wheel, and the parametric equations of the effective grinding edge are formulated. The wheel orientation and location in 5-axis flute grinding are calculated automatically and accurately so that the rake angle of the rake face and the core radius are ensured. The new approach is verified with several examples in this work. Therefore, it can improve the end-mill quality and lays a good foundation for the computer-aided design/computer-aided engineering/computer-aided manufacturing (CAD/CAE/CAM) of end-mills.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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 | 0.000 |
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".