Towards Reliability-Enhanced Mechanical Characterization of Non-Crimp Fabrics: How to Compare Two Force-Displacement Curves against a Null Material Hypothesis
Why this work is in the frame
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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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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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 it