Mathematical modelling and optimization of surface quality and productivity in turning process of AISI 12L14 free-cutting Steel
Why this work is in the frame
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Bibliographic record
Abstract
In this study, several series of experiments on turning process of AISI 12L14 free cutting steel characterized by its self-lubrication and the high percentage of lead in its composition were performed to rate the influence of cutting conditions (Vc, f and ap) on the machining performance such as surface roughness, cutting force, cutting power and material removal rate. A computer generated optimal design of experiment based on the I-optimality criteria along with analysis of variance was created to study the characterizations in turning of this steel, and desirability function was utilized for the optimization. The global optimization, combined high surface quality and productivity with low cutting power consumption, gave 12 optimal setting points provided high desirability values. The obtained correlation for surface roughness, cutting force, material removal rate and cutting power were 99.4%, 95.5%, 99.7% and 94.3%, respectively.
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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 | 0.001 | 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