Effect of circular tool path on cutting force profile in micro-end-milling
Bibliographic record
Abstract
Micro-end-milling requires high spindle rotational speed to achieve effective material removal. This results in the requirement of tool stoppage or slowdown during a micro-end-milling operation, a deterrent to productivity and to part acceptability. A circular tool path geometry can avoid discontinuities in the tool movements leading to a more consistent and smooth material removal. However, optimal process planning for such a tool path will require detailed understanding of the chip-formation mechanism in circular end-milling. The cut geometry during end-milling along a circular tool path is often approximated as that of a linear tool path. Although this assumption works well for circular tool paths with higher tool path radius, this is not the case for lower tool path radius often used in micro-milling. In this study, the effect of circular tool path on the cutting force for varying tool path rotation angle, tool path radius, and feed rate is experimentally investigated. Systematic signal processing was applied to analyse the measured cutting force signal along linear and several circular tool paths. Qualitative as well as quantitative differences were observed in the cutting force profiles obtained using different tool path radii, tool path orientations, and feed rates. This implies the need for an improved chip thickness formulation dedicated to micro-end-milling with circular tool path rather than approximating it with formulations derived for linear tool path.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".