Cutting performance comparison of two different diamond segments used in frame saw for granite
Bibliographic record
Abstract
Blades with concave segments and multi-layered segments were employed to cut large-sized granite (size: 2000 mm × 2000 mm × 2000 mm) using a diamond frame saw, which can accommodate 80–120 blades (size: 4000 mm × 180 mm × 3.5 mm). Cutting force was determined by a Kistler dynamometer, and diamond segment wear was examined by scanning electron microscopy and Keyence microscopy. Sawing performance was evaluated based on slab quality, radial wear, cutting force (force ratio), and slab production rate. Compared with the blade with multi-layered segments, experimental results showed that the blade with concave segments produced better slab quality; lower cutting forces, force ratio (normal force/horizontal force), and radial wear (tool consumption); and higher slab production rate. Experiments indicate that blades with concave diamond segments are more suitable for the diamond frame saw to cut granite.
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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 | 0.000 |
| 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".