Multiscale Compressive Failure Analysis of Wrinkled Laminates Based on Multiaxial Damage Model
Bibliographic record
Abstract
The waviness defect, a common manufacturing flaw in composite structures, can significantly impact the mechanical performance. This study investigates the effects of wrinkles on the ultimate load and failure modes of two Carbon Fiber Reinforced Composite (CFRC) laminates through compressive experiments and simulation analyses. The laminates have stacking sequences of [0]10S and [45/0/−45/90/45/0/−45/0/45/0]S. Each laminate includes four different waviness ratios (the ratio of wrinkle amplitude to laminate thickness) of 0%, 10%, 20% and 30%. In the simulation, a novel multiaxial progressive damage model is implemented via the user material (UMAT) subroutine to predict the compressive failure behavior of wrinkled composite laminates. This multiscale analysis framework innovatively features a 7 × 7 generalized method of cells coupled with stress-based multiaxial Hashin failure criteria to accurately analyze the impact of wrinkle defects on structural performance and efficiently transfer macro-microscopic damage variables. When any microscopic subcell within the representative unit cell (RUC) satisfies a failure criterion, its stiffness matrix is reduced to a nominal value, and the corresponding failure modes are tracked through state variables. When more than 50% fiber subcells fail in the fiber direction or more than 50% matrix subcells fail in the transverse or thickness direction, it indicates that the RUC has experienced the corresponding failure modes, which are the tensile or compressive failure of fibers, matrix, or delamination in the three axial directions. This multiscale model accurately predicted the load–displacement curves and failure modes of wrinkled composites under compressive load, showing good agreement with experimental results. The analysis results indicate that wrinkle defects can reduce the ultimate load-carrying capacity and promote local buckling deformation at the wrinkled region, leading to changes in damage distribution and failure modes.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".