Surface roughness evaluation of various cutting materials in hard turning of AISI H11
Why this work is in the frame
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Bibliographic record
Abstract
This paper describes a comparison of surface roughness between ceramics and cubic boron nitride (CBN7020) cutting tools when machining of AISI H11 hot work steels treated at 50 HRC. Plan is designed according to Taguchi's L18 (2132) orthogonal array. The response surface methodology (RSM) and analysis of variance (ANOVA) were used to check the validity of multiple linear regression models and to determine the effects, contribution, significance and optimal machine settings of process parameters, namely, cutting speed, feed rate and depth of cut on machining parameters on the Ra and Rt. The results of this research work showed that, the feed rate was found to be a dominant factor on the surface roughness, followed by the cutting speed, lastly the depth of cut. The CBN7020 cutting tool showed the better performance than that of ceramic based cutting tool. In addition, the combination of low feed rate and high cutting speed is necessary for minimizing the surface roughness.
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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 | 0.002 | 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 it