A multiscale framework for predicting the mechanical properties of unidirectional non-crimp fabric composites with manufacturing induced defects
Bibliographic record
Abstract
The aim of this study was to evaluate the effect of manufacturing induced in-plane tow misalignment and out-of-plane tow crimp on the mechanical properties of a heavy-tow unidirectional non-crimp fabric (UD-NCF) composite. The elastic constants and failure onset (strength) are predicted by employing a multiscale computational approach. Micro-scale finite element (FE) models that explicitly represent the fibers and matrix within the tow microstructure were used to predict the effective properties of the tow. Meso-scale FE models comprised of the homogenized tows and surrounding matrix were used to predict the properties of a UD NCF composite lamina. Four meso-scale models, identified as ideal, crimp, misalignment and real, were considered in this study. No manufacturing defects were represented in the ideal model, while out-of-plane crimp, in-plane misalignment and both out-of-plane crimp and in-plane misalignment were accounted for in the crimp model, misalignment model and real model, respectively. Predicted lamina stiffness based on the real model are found to be in an excellent agreement with available experimental data, which was not always the case for the other three models. The longitudinal and transverse strength predictions are found to be dependent on the chosen local failure criteria for each model. Max-stress and Puck’s fiber failure criteria provide an excellent estimate of longitudinal strength while the Puck’s inter-fiber failure and Tsai-Hill criteria predict transverse strength with good accuracy. The feasibility to accurately predict the mechanical properties of heavy tow non-crimp fabric composites by incorporating their inherent micro-structural defects is demonstrated in this study.
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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) | 0.000 | 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".