Experimental Investigation on Part Quality and Dust Emission during Minimum Quantity Lubricated (MQL) Edge Finishing of Granite
Bibliographic record
Abstract
Edge-finishing of granites by grinding is a process frequently used in the granite processing industry to generate the final desired shape and edge quality of products. However, this process releases significant amounts of fine and ultrafine particles (FPs and UFPs) containing crystalline silica. When inhaled, this dust can cause silicosis disease and threaten the health and safety of workers. The purpose of this study is to optimize the process by decreasing the concentrations of dust generated while also maintaining the required surface finish. Experimental tests were planned and performed on granite samples using a full factorial design. Two cutting tool edge shapes were studied (chamfer and concave) using G150 and G600 grit size tools, at various spindle speeds (1500, 2500, 3500 rpm), feed rates (500, 1000, 1500 mm/min) and lubrication flow rates (20, 40, 60 mL/min). The findings show that the particle emissions as well as the surface finish depend on the tool shape, its grit size, and the machining and lubrication parameters used. Higher MQL flow rates led to better finished surface quality and lower concentrations of fine dust. Polishing with flood lubrication reduces the maximum number concentration of FPs corresponding to particles smaller than 1 µm diameter by about 85% as compared to dry polishing and produced the best surface finish. Polishing with lubrication in MQL mode at 60 mL/min led to the production of part with Ra-value comparable with that obtained in flood lubrication condition.
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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) | 0.001 | 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 | 0.001 | 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".