Prediction of Damage in Non-Crimp Fabric Composites Subjected to Transverse Crushing: A Comparison of Two Constitutive Models
Bibliographic record
Abstract
Non-crimp fabrics (NCFs) are increasingly used in industry for manufacturing of composite structures due to a combination of high mechanical properties and excellent manufacturability. As with other composites, in-service damage can be a cause for severe reduction in load-carrying capacity of NCF-reinforced plastics. In this experimental and numerical study, two constitutive material models previously used only for damage prediction in unidirectional (UD) tape and woven fabric-reinforced materials (LS-DYNA’s *MAT_ENHANCED_COMPOSITE_DAMAGE—MAT54 and *MAT_LAMINATED_COMPOSITE_FABRIC—MAT58) were evaluated for simulating transverse crushing of composite parts processed from a non-crimp carbon fabric. For this purpose, UD NCF components of tubular shape were subjected to transverse crushing through a controlled indentation of a metallic cylinder, and results of the experiment were compared with numerical modeling. Considered verification metrics included the observed and the predicted patterns of interlaminar damage, the extent of delamination, as well as the ability of the models to replicate force-displacement response exhibited by the tested specimens.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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) | 0.001 | 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".