Optimization of wet edge finishing of natural stones
Bibliographic record
Abstract
Edge polishing is a critical process in stones manufacturing and transformation as this process affects both aesthetic quality and manufacturing efficiency. This study investigates the impact of tool geometry, grit size, and cutting conditions on surface quality, tool wear, and process optimization in wet edge polishing of granites. Four tool shapes, eased concave edge, eased chamfered edge, ogee edge, and half-beveled edge, were used, each with three grit sizes (G150, G300, G600). Cutting parameters included spindle speeds of 1500, 2500, and 3500 rpm, along with feed rates of 500, 1000, and 1500 mm/min.Surface quality was assessed by measuring roughness profiles and Ra-values under varying conditions, while tool wear and cutting forces were analyzed to evaluate polishing efficiency. Results indicate that finer grits improve surface finish but increase processing time and tool wear. Higher spindle speeds enhance material removal but generate greater cutting forces, potentially affecting tool longevity. The interaction between edge geometry and process parameters revealed that eased concave and ogee edges require lower energy input and produce superior surface finishes.Process optimization was performed to identify the best balance between surface quality and efficiency. Statistical analysis demonstrated that an optimal combination of G600 grit, a spindle speed of 2500 rpm, and a feed rate of 1000 mm/min provides a favorable compromise between polishing performance and tool durability. Additionally, cutting forces obtained during the edge finishing of black granite were marginally higher than those obtained for white granite. These findings contribute to improving the granite edge polishing process by optimizing cutting parameters to enhance productivity while maintaining high-quality standards.
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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 | 0.000 | 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) | 0.000 | 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 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".