Evaluation of Simple Shear Test Geometries for Constitutive Characterization using Virtual Experiments
Bibliographic record
Abstract
Abstract In-plane simple shear tests have become commonplace in the fracture characterization of automotive sheet metals but have received less attention for constitutive characterization. Unlike tensile tests, simple shear tests do not have any tensile instability and remain in a state of plane stress until fracture. From plastic work equivalence, an isotropic hardening model can be readily constructed from the tensile and shear test data without inverse finite-element analysis. The success of the methodology hinges upon the shear specimen geometry and how the local strains in the gage region are measured using digital image correlation (DIC). In this study, finite-element simulations of seven shear test geometries were evaluated for an isotropic material in a series of virtual experiments by varying the input hardening response. The data from the simulations was extracted from the surface as if DIC was employed and used to determine the hardening behavior in comparison with the exact solution. Shear geometries without a notch eccentricity in the gage region appear to be best suited for characterizing low hardening materials with an error of less than 1% in the stress response for an n-value of 0.02. Conversely, for higher hardening materials corresponding to an n-value of 0.20 or greater, the geometries with a notch eccentricity performed best.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| 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 | 0.001 |
| 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 teacher head, 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".