Predicting the forming limit diagram of the fine-grained AA 1050 sheet using GTN damage model with experimental verifications
Bibliographic record
Abstract
In this research, forming limit diagram (FLD) of aluminum alloy 1050 (AA 1050) sheet produced by Accumulative Roll Bonding (ARB) is investigated numerically and experimentally. The Gurson-Tvergaard-Needleman (GTN) ductile damage model is used to predict sheet failure and obtain its FLD using numerical simulation in Abaqus/Explicit. Nucleation and growth of voids in the material during the deformation is the basic concept of the GTN damage model. This damage model has nine basic parameters that obtaining through experimental tests is time-consuming and costly, and in some cases, impossible. Thus, the present study tries to obtain the above parameters for fine-grained aluminum 1050 fabricated by ARB using the finite element method. Therefore, after considering each parameter’s interval, numerical simulation and the anti-inference method are used in the uniaxial tensile test to identify GTN parameters for the AA 1050 sheet using FEM. The optimum parameters of the GTN model are used in the FEM of the Nakazima test for FLD prediction. Also, The FLD of the fine-grained aluminum sheet is obtained experimentally using the Nakazima test. Finally, the numerical and experimental FLDs are compared for validation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".