Identification of Material Constitutive Laws for Machining—Part II: Generation of the Constitutive Data and Validation of the Constitutive Law
Bibliographic record
Abstract
This paper presents an integral methodology to obtain a wide range of constitutive data required for the identification of the constitutive equation used in simulating cutting processes. This methodology is based on combining the distributed primary zone deformation (DPZD) model developed in Part I (Shi et al., 2010, ASME J. Manuf. Sci. Eng., 132, p. 051008.) of this study with quasi-static indentation (QSI) tests, orthogonal cutting tests at room temperature (RT) and high temperature. The QSI tests are used to capture the material properties in the quasi-static conditions, which solve the unstable solutions for the coefficients of the constitutive law. The RT cutting tests are designed to fulfill the assumptions embedded in the developed DPZD model in order to provide the distributed constitutive data encountered in the primary shear zone. To capture the material behavior in the secondary shear zone, the orthogonal cutting tests with a laser preheating system are designed to raise the temperature in the primary zone to the level encountered in the secondary zone. As an application of the generated constitutive data, the Johnson–Cook model is identified for Inconel 718. This constitutive law is further validated using high speed split Hopkinson pressure bar tests and orthogonal cutting tests combined with finite element simulations. In comparison with the previous approaches reported in the open literature, the developed DPZD model and methodology significantly improve the accuracy of the simulation results.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".