CALIBRATION OF THE JOHNSON-COOK FAILURE PARAMETERS AS THE CHIP SEPARATION CRITERION IN THE MODELLING OF THE ORTHOGONAL METAL CUTTING PROCESS
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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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".