Predicting the Ductile Failure of DP-steels Using Micromechanical Modeling of Cells
Bibliographic record
Abstract
Thus far, micromechanical modeling of cells has been used successfully to capture the deformation behavior of dual phase (DP) steels, which display impressive mechanical properties, especially for the automotive industry. However, the prediction of ductile failure, which is essential in the manufacture and design of parts, needs to be modeled in order to develop a model, which can fully characterize DP-steels. The Gurson—Tvergaard (GT) damage model is coupled with a micromechanical model developed in earlier works, which captures the deformation behavior of DP-steels well, making a complete material model. A procedure that accounts for damage in terms of the void volume fraction, stress triaxiality and the mechanics of failure in DP-steels as major damage factors, is developed in this work to determine the calibrating parameters in the GT yield function. When these parameters are determined, they are employed in numerical simulations of a tensile bar test to compare the experimental and numerical fracture parameters. The results show good agreement between the numerical predictions using the GT parameters obtained by the procedure developed in the current work and the experimental findings at different levels of volume fraction of martensite (V m ). It is also shown that the GT parameters obtained using a calibrating procedure, which ignores the local deformation behavior of the material, does not produce the appropriate parameter values.
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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 | 0.000 | 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 | 0.001 | 0.000 |
| 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".