Modeling of Bending and Bend-Stretching of Laminated Aluminum Sheets
Bibliographic record
Abstract
Bending and subsequent stretching of sheet materials is typical of many sheet forming operations. Bending and bend-stretching characteristics and limit strain of monolithic AA2024 and laminated tri-layer Alclad 2024 aluminum sheet materials are studied by modeling and experimentation. A computationally efficient analytical model based on advanced bending theory is developed for the laminated sheet materials and utilized to predict the bending characteristics of the above sheet materials. The effects of cladding thickness ratio on the bending characteristics of laminated sheet are compared with the monolithic constituent. Also, predictions from the above analytical bending model are compared with 2D and 3D FE-based bending models. In addition, bend-stretching experiments are conducted using a specialized test jig while continuously recording images using dual-camera set-up from tensile surface and edge of the specimen. A stochastic pattern is applied to the specimen prior to the test and the images are later processed to analyze the development and localization of strains based on digital image correlation (or DIC) method. Strain maps from DIC analysis are utilized to determine the limit strain in the vicinity of the bend line, as well as from FE modeling of bend-stretching tests, using maximum major strain acceleration criterion for localized necking proposed by one of the authors. The results from experimental and modeling work indicate higher limit strains in bend-stretching for Alclad 2024 compared to monolithic AA2024 sheet.
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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 | 0.001 | 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".