Effect of Milling Parameters on Surface Characteristics and Mechanical Properties of Presintered Zirconia Ceramic
Bibliographic record
Abstract
In this research, a computer with integrated software connected to a Computer Numerical Control (CNC) milling machine was used to machine presintered Aconia zirconia samples as per the ASTMC1161-13 standard.The aim was to improve the surface characteristics and mechanical properties of presintered zirconia ceramic by varying cutting conditions such as depth of cut (0.1, 0.3, and 0.5 mm), rotational speed (6000, 9000, and 12000 rpm), and tool diameter (1.5 and 2.5 mm).Before sintering, using a bur diameter of 2.5 mm yielded the lowest roughness value of 0.76 µm at 9000 rpm with a depth of 0.1 mm.The surface roughness decreased as the cutting speed increased and cutting depth decreased.After sintering at 1500℃ for 2 hours at a rate of 8℃/min, the maximum hardness value of 1458.73HV was obtained at 6000 rpm using a bur diameter of 2.5 mm.The highest flexural strength value of 566.67 MPa was obtained at a rotational speed of 6000 rpm, depth of cut of 0.1 mm, and tool diameter of 1.5 mm.The highest surface fracture toughness value of 414.94 MPa/mm 2 was obtained at a rotational speed of 12000 rpm, depth of cut of 0.5 mm, and tool diameter of 2.5 mm.Additionally, the use of high cutting speed during the milling processes reduced the mechanical properties of the Aconia zirconia, such as flexural strength and microhardness, when using a tool diameter of 1.5 mm.However, these mechanical properties increased when using a tool diameter of 2.5 mm.
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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 | 0.001 | 0.002 |
| 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".