Influence of Surface Defect Geometry on the Localization and Failure of AA6111 Sheet: Necking versus Shear
Bibliographic record
Abstract
The influence of surface defect geometry on the localization and failure behaviour of AA6111 sheet has been investigated through experimentation and numerical modelling. A series of uniaxial tensile samples were produced with idealized top and bottom surface defects (i.e. grooves), located either symmetrically or asymmetrically on the opposing surfaces. The symmetric arrangement corresponds to the “groove-like” initial imperfection of the classical Marciniak- Kuczyński (M-K) model. Experimental results indicate that both the symmetry of the defects and their wavelength have a profound effect on the resulting mode of localization and failure as well as on the limit strains. Specifically, symmetric surface defects are seen to induce localization and failure through simple necking, whereas asymmetric defects tend to promote macroscopic, throughthickness shearing. Furthermore, asymmetric surface defect geometries are found to produce lower limit strains in the AA6111 sheet under study for defect wavelengths below about 1.5 mm, while the reverse is true when defect wavelengths are above 1.5 mm. Finite element method (FEM) modelling simulations are also presented, demonstrating that the experimentally-observed trends in localization and failure behaviour can be replicated using a mixed isotropic-kinematic hardening implementation of the Gurson-Tvergaard-Needleman (GTN) material model.
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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 | 0.001 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".