CNC Edge Finishing of Granite: Effect of Machining Conditions on Part Quality, Cutting Forces, and Particle Emissions
Bibliographic record
Abstract
Edge finishing is a shaping process that is extremely important in the granite and marble processing industries. It does not only shape the edge but also makes it shiny and durable. However, this process generates dust (fine and ultrafine particles) that can have a significant impact on air quality in the workshop and can put workers' health at risk. While environmental requirements and occupational health and safety regulations are becoming increasingly stringent, at the same time, industries must continue to produce quality parts at competitive prices. The purpose of this study was to examine the surface quality, the cutting forces, and the emission of fine (FP) and ultrafine (UFP) particles during wet and dry edge finishing of granite edges as a function of the machining parameters and abrasive grit sizes. Three machining operations were investigated: roughing, semi-finishing, and finishing, using diamond abrasives (with grit sizes 45, 150, 300, 600, 1500, and 3000). The experiments were carried out on two granites, one being black and the other white. The tested spindle speeds ranged from 1500 rpm to 3500 rpm and the feed rates from 500-1500 mm/min. It was found that roughing operations produce more fine particles while finishing operations produce more ultrafine particles. These particle emissions, as well as the part quality and the cutting forces are strongly dependent on cutting speed and on the grit size of the abrasive used.
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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".