Characters of Deformation and Surface Quality and Cutter Wearing at High-Speed Cutting
Bibliographic record
Abstract
In this paper, the impact of cutting speed and feed amount on cutting deformation and surface quality and cutter wear have been studied by the test of high manganese steel cutting with metal ceramic tool at high speed. The results show that it is feasible by cutting high manganese steel with cermet tool at high-speed cutting (HSC). Chip changes into succession segment from band in macrostate. Sawteeth can be seen on the top surface of chip while high temperature feature in burning color and cold welding form appear on the bottom of the chip. The model of surface roughness built can be used to predict surface quality at HSC, and provided cutting optimized parameters scheme. Further research indicates that high-speed cutting has its special deformation mechanism and plastic chips exhibit hot brittleness which provides a favorable condition for solving the chip breaking problem in the cutting of high manganese steel.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".