Evaluation of the VDA 238-100 Tight Radius Bending Test using Digital Image Correlation Strain Measurement
Bibliographic record
Abstract
The VDA238-100 standard for tight radius bending (v-bending) of sheet materials has received widespread acceptance with automotive suppliers and material producers to characterize local formability. However, the test fixture and tooling in the v-bend test standard is not amenable to direct strain measurement and the operator cannot terminate the test at the onset of crack initiation as the outer bend surface is not visible. Consequently, fracture is identified using a load threshold and the bend angle estimated from an analytical formula based upon the punch displacement and tooling geometry. Bend angles are not directly transferable and must be interpreted relative to the sheet thickness and bend radius unlike a strain measurement. By obtaining an in-situ strain measurement on the surface using digital image correlation (DIC), the plane strain fracture limit can be accurately identified at the onset of cracking and remove ambiguity in translating the bend angles to practical forming operations and simulations. A novel inverted VDA test frame was developed to incorporate DIC strain measurement during the bend test and a variety of advanced high strength sheet materials were evaluated. It was observed that the VDA bend test creates a homogeneous strain state of plane strain across the width of the sample along with a proportional strain path to fracture without necking that is ideal for fracture characterization. A correlation is developed to relate the bend angle with the major strain for the materials considered and accounts for the sheet thickness and bend radius. A comparison of the bend angle obtained using the formula in the VDA standard based on the punch displacement was in very good agreement with manual measurements and an algorithm to measure the bend angle using DIC analysis was developed.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".