Towards Reliability-Enhanced Mechanical Characterization of Non-Crimp Fabrics: How to Compare Two Force-Displacement Curves against a Null Material Hypothesis
Bibliographic record
Abstract
Detailed characterization of fabric reinforcements is necessary to ensure the quality of manufactured composite parts, and subsequently to prevent structural failure during service. A lack of consensus and standardization exists in selecting test methods for the mechanical characterization of fabrics. Moreover, in reality, during any experimentation there are sources of uncertainties which may result in inconsistencies in the interpretation of data and the comparison of different testing methods. The aim of this article is to show how simple statistical data analysis methods may be used to enhance the characterization of composite fabrics under individual and combined loading modes while accounting for inherent material/test uncertainties. Results using a typical glass non-crimp fabric (NCF) show that, statistically, there are significant differences between the warp and weft direction responses of a presumably balanced NCF under all deformation modes, with weft yarns being generally stiffer. Moreover, the statistical significance of warp-weft couplings under both simultaneous and sequential biaxial-shear loading modes became statistically evident, when compared to a pure biaxial deformation.
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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 | 0.020 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.003 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".