A Comparison of Discrete Damage Modeling Methods: The Effect of Stacking Sequence on Progressive Failure of the Skin Laminate in a Composite Pi-joint Subject to Pull-off Load
Bibliographic record
Abstract
View Video Presentation: https://doi.org/10.2514/6.2021-0571.vid Discrete damage modeling (DDM) of composite failure mechanisms including delamination, matrix cracking, and their interactions was performed for the skin laminate in a composite pi-joint test specimen subject to a pull-off load. The skin laminate stacking sequence was varied, and the pull-off load and predicted damage path were documented. Within the study the prediction of two DDM tools were compared, Abaqus XFEM and B-Spline Analysis Method (BSAM). Both DDM tools use similar methodology for determining sites of damage initiation and use cohesive zone models for damage accumulation and crack propagation. However, the tools differ in their approach of modeling mesh independent matrix cracks in the bulk lamina. Abaqus XFEM implements a standard formulation of the eXtended Finite Element Method (XFEM), whereas BSAM uses a Regularized eXtended Finite Element Method (RX-FEM). A parametric model was developed using a python script to automate the required preprocessing. The results of the DDM tools were compared with baseline models that only considered interface damage. It was shown that by including effects of matrix cracks the peak pull-off loads were considerably reduced. Moreover, the predicted damage path between the baseline and DDM models were vastly different. Comparing the two DDM tools, the predictions of the damage initiation locations were in agreement, and the predicted damage paths and peak pull-off loads were similar. However, the Abaqus solver exhibited convergence issues when damage occurred at multiple sites and the damage interaction became complex, whereas the BSAM solver did not.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".