Analysis of Nonisothermal Deep Drawing of Aluminum Alloy Sheet With Induced Anisotropy and Rate Sensitivity at Elevated Temperatures
Bibliographic record
Abstract
In this paper, a finite element model is developed for 3000 series clad aluminum alloy brazing sheet to account for temperature and strain rate dependency, as well as plastic anisotropy. The current work considers a novel implementation of the Barlat YLD2000 yield surface in conjunction with the Bergstrom hardening model to accurately model aluminum alloy sheet during warm forming. The Barlat YLD2000 yield criterion is used to capture the anisotropy while the Bergstrom hardening rule predicts the temperature and strain rate dependency. The results are compared with those obtained from experiments. The measured stress–strain curves of the AA3003 aluminum alloy sheet at elevated temperatures and different strain rates are used to fit the Bergstrom parameters and measured R-values and directional yield stresses are used to fit the yield function parameters. Isothermal uniaxial tensile tests and nonisothermal deep drawing experiments are performed and the predicted response using the new constitutive model is compared with measured data. In simulations of tensile tests, the material behavior is predicted accurately by the numerical models. Also, the nonisothermal deep drawing simulations are able to predict the load–displacement response and strain distributions accurately.
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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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".