Assessment of Analytical Models of Pure Bending of Sheet Materials Using the Digital Image Correlation Method
Bibliographic record
Abstract
Abstract Bending of sheet materials is a common forming mode for shaping sheet components. Although many numerical models of bending, both analytical and numerical simulations based, are available in the literature, extensive experimental validations have been rather limited. A new bend test method and complementary three‐dimensional finite element (FE) simulation of the experiments are employed to assess the predictions from an advanced analytical and FE model of pure bending of aluminium sheet materials. The experimental set‐up developed and utilised is an open concept design that allows access to the tensile surface and through‐thickness region in the vicinity of the specimen bend line to continuously record images of the deforming specimen with two cameras. The specimen images are analysed for strains using an online strain mapping system based on digital image correction method. Tangential strain distribution results from the models in terms of material thinning in the bend region are compared with those from the experiments on AA2024 aluminium sheet material by considering the responses from the specimen edges and mid‐width regions at the bend line. Furthermore, the tangential and radial stress distributions on the through‐thickness section of the specimen from the analytical model are compared with those from the FE model. The results from experiments, FE model and analytical model are compared and discussed in the light of the experimental data and the assumptions involved in the development of the 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".