CALIBRATION OF THE JOHNSON-COOK FAILURE PARAMETERS AS THE CHIP SEPARATION CRITERION IN THE MODELLING OF THE ORTHOGONAL METAL CUTTING PROCESS
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Bibliographic record
Abstract
The finite element analysis (FEA) is a numerical method widely used to predict the metal-cutting performance in both academic and industrial studies, avoiding the high expense and time consumption of experimental methods. The problem is how to calibrate reliable fracture-parameters as chip-separation criterion are implemented into FEA modelling. This thesis introduces a calibration method of the Johnson-Cook fracture parameters used in the orthogonal metal cutting modelling with a positive rake angle for AISI 1045 steel. These fracture parameters were obtained based on a set of quasi-static tensile tests, with smooth and pre-notched round bars at room temperature and elevated temperatures. The fracture parameters were validated by low- and high-strain rate simulations corresponding to tensile tests and orthogonal metal-cutting processes respectively in ABAQUS/Explicit. Compared to literature calibration methods, this method is simpler, less expensive but valid.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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