An efficient full-field crystal plasticity-based M–K framework to study the effect of 3D microstructural features on the formability of polycrystalline materials
Bibliographic record
Abstract
Abstract In this paper, the new rate tangent–fast Fourier transform-based elasto-viscoplastic crystal plasticity (CP) constitutive framework (RTCP-FFT) developed by Nagra et al (2017 Int. J. Plast. 98 65–82) is implemented in the so-called Marciniak–Kuczynski (M–K) (Marciniak and Kuczyński 1967 Int. J. Mech. Sci. 9 609–20) framework to predict the forming limit diagrams (FLDs) of face-centered cubic polycrystals. The RTCP-FFT approach that accounts for 3D grain morphologies and grain interactions is used to compute the FLDs for aluminum alloys (AAs). The model employs two statistically representative volume elements with identical initial microstructures, one inside the imperfection band region (required for M–K analysis) and other outside the imperfection band region of the sheet metal. The proposed RTCP-FFT-based M–K model is a full-field, mesh-free and efficient CP formulation that enables a comprehensive investigation of the effects of 3D microstructural features on the FLDs with extremely small computational times. The new model is validated by comparing the predicted FLDs for AA5754 and AA3003 AAs with experimental measurements. Furthermore, the predicted FLDs are compared with the well-known Taylor-type homogenization scheme-based M–K model (MK–Taylor) predictions. Furthermore, the effects of different grain shapes as well as local grain interactions on the FLD predictions are studied. The study reveals that among the various microstructural features, the grain morphology has the strongest effect on the predicted FLDs and the FLD predictions can be significantly improved if the actual grain structure of the material is properly accounted for in the numerical models.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 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".