Three-point bending analysis with cohesive surface interaction for improved delamination prediction and application of carbon fibre reinforced plastics composites
Bibliographic record
Abstract
Abstract Carbon fibre reinforced plastics laminates were loaded through to fracture in a three-point bending configuration, to gain understanding of the cohesive interaction between plies and validate mechanical properties and predictive capability of the FE model. The effect of mesh refinement, scaling techniques, failure models and cohesive surfaces were investigated. Fibre orientations investigated were parallel, 45° and perpendicular to the loading. Experimental results showed a larger radius punch promoted failure on the intended bottom side, tensile stresses region, allowing for the Aramis strain camera to record the failure. When the fibre orientation was perpendicular to the punch load, all failure models show similar rate of force increment with respect to displacement. No difference in failure prediction is observed for the different 0° models, except for a 4.18% under prediction by LaRC02 compared to the experiment. With fibre orientations at 45° and 90°, the Maximum Strain and LaRCO2 failure models were more suitable in terms of accuracy and convergence. Incorporating cohesive surfaces between instances improve nonlinearity prediction of 45° and 90° layups. Small span-to-thickness ratio analysis predicts interlaminar shear failure, delamination, versus large span-to-thickness ratio determine normal stresses to dominate failure in laminate. The model was setup in multi-fibre orientation and cross-ply layups for extended application and was shown to successfully predict material response described in literature.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".