Evaluation criteria of the constitutive law formulation for the metal-cutting process
Bibliographic record
Abstract
Modelling of the cutting process is necessary to predict cutting forces, residual stresses, and burr formation. A major difficulty in this modelling process is the description of the material behaviour in the primary and the secondary deformation zones, which is characterized by severe plastic deformation at high temperatures and strain rates. The description of the material behaviour requires correct formulation of the constitutive law. Although a number of formulations have been proposed to capture the flow stress behaviour, the assessment of these formulations for the cutting process is still a very difficult task owing to the lack of direct measurements of the high strains, strain rates, and temperatures encountered in the cutting process. This paper presents novel evaluation criteria to assess the degree of accuracy of the constitutive equation under machining conditions. Different existing constitutive laws are identified for Inconel 718, and then evaluated using the proposed criteria. To better describe the plastic behaviour of Inconel 718, new constitutive relationships are formulated and evaluated. From the evaluation results, an accurate description of the constitutive relationship for Inconel 718 is established. This constitutive law is further validated using high-speed split Hopkinson pressure bar (SHPB) tests and orthogonal cutting tests in conjunction with finite element simulations.
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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 | 0.008 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".