Experimental Investigation on the Effects of Tool Geometry and Cutting Conditions on Machining Behavior during Edge Finishing of Granite Using Concave and Chamfered Profiling Tools
Bibliographic record
Abstract
Granite edge finishing through grinding is a common process in the granite processing industry, crucial for achieving the final desired shape and edge quality of products. This study focuses on the granite industry, specifically delving into the significance of grinding and polishing for improving aesthetics and extending material longevity. The experimental design entails a comprehensive factorial experiment plan involving two workpiece materials (white and black granite samples) and two cutting tool edge shapes (chamfer and concave), each with two grit sizes: G150 and G600. The cutting conditions varied and consisted of variations in spindle speeds (1500, 2500, 3500 rpm), feed rates (500, 1000, 1500 mm/min), and lubrication modes (wet/dry). The results uncover intricate relationships among these parameters and part quality, underscoring the pivotal role of tool geometry in achieving superior surface finishes and in controlling the cutting forces. These findings contribute to a nuanced understanding of the dynamic interplay between tool characteristics, material properties, and machining conditions within the granite industry.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".