Cutting Force and Cutting Quality during Tapered Milling of Glass Magnesium Board
Bibliographic record
Abstract
In this paper, the effects of tool geometry and cutting parameters on cutting force and quality were investigated during the tapered milling of glass magnesium (MGO) board with diamond cutters. The results were as follows: firstly, both the cutting force and roughness of the machined surface were positively correlated with the taper angle of the cutters and the cutting depth, but negatively related to the spindle speed. Then, the cutting depth had the largest influence on the cutting force and surface roughness, followed by the taper angle and spindle speed. Thirdly, the taper angle had a significant influence on the cutting force, but not on the surface roughness. The contribution of the spindle speed to both the cutting force and the surface roughness were significant, while the cutting depth had an insignificant effect on the cutting force and the surface roughness. Finally, the optimal cutting condition for the tapered milling of glass magnesium board was found to be a taper angle of 15°, a spindle speed of 5000 rpm (cutting speed of 36.63 m/s), and a cutting depth of 0.5 mm, which are proposed for industrial production in order to achieve greater cutting quality and economic benefit.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".