Modeling of Delamination Initiation and Propagation in Composite Laminates Under Monotonie Tensile Loading Using the Progressive Damage Modeling Technique
Bibliographic record
Abstract
This paper introduces the preliminary validation of a 3-D numerical model based on the progressive damage modeling technique.Similar models have been reported in recent literature, attempting to study interlaminar stress distribution, static failure load and fatigue life of laminates, as well as delamination propagation in laminated plates under cyclic loading.The present model is intended to predict delamination initiation and propagation in laminates without stress raisers under monotonic complex loading.The validation process includes a comparative study on the performance of both SOLID46 and SOLID 191 elements using the Ansys Finite Element (FE) software, in addition to a study on the use of symmetry conditions in the FE modeling of composites.The main issues involved in the validation are: a) the ability of the model to capture interlaminar stresses near the free edge of the laminate, and b) the accuracy of progressive damage laws in representing delamination propagation in a laminate under monotonic tensile loading.A good correlation was achieved between numerical results and data in the literature for both issues.Also, the study on the use of symmetry conditions showed some singularities in interlaminar stress distribution, in the vicinity of the symmetry plane.Therefore, it was concluded that particular attention should be paid to the interlaminar stress distribution in the zone near the symmetry planes when using solid modeling.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 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".