Advanced measurement techniques for braided composite structures: A review of current and upcoming trends
Bibliographic record
Abstract
Braiding is an advanced textile manufacturing method that is used to produce two-dimensional and three-dimensional components. Unlike laminated structures, braids have interlaced yarns that form a continuity between layers. This structure allows for improved impact resistance, damage tolerance, and improved through-thickness reinforcement. Despite the numerous advantages of braided composites, braids also have shortcomings. Their highly complex fiber architecture presents challenges in the availability and choice of the strain measuring and characterization techniques. Advanced measurement methods such as optical strain measurement, micro-computed tomography, and in situ strain measurement are required. Optical strain measurement methods such as digital image correlation and high-speed imaging are necessary to accurately measure the complex deformation and failure that braided composites exhibit. X-ray-based micro-computed tomography measurements can provide detailed geometric and morphologic information for braided structures, which is necessary for accurately predicting the mechanical properties of braided structures. Finally, in situ strain measurement methods will provide detailed information on the internal deformation and strain that exists within braided structures. In situ sensors will also allow for in-service health monitoring of braided structures. This paper provides a detailed review of the aforementioned sensing technologies and their relation to the measurement of braided composite structures.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".