Non-destructive evaluation of stacking sequence in textile composite: different techniques and experimental verification
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose – The purpose of this paper is to experimentally investigate the capability of four non-destructive testing (NDT) techniques to detect the layer orientation in textile composite laminates. The aerospace industry has been the primary driving force in the use of textile composites. Design/methodology/approach – Woven glass fiber composite samples were inspected using C-scan ultrasonic, vibration analyzer, X-ray micro-tomography and ultraviolet technique. In a complementary study, mechanical testing was carried out to investigate the effect of mid-layer orientation on in-plane tensile strength and their failure modes using microscopic imagining. Findings – During C-scan ultrasonic, the high attenuation and scattering of ultrasonic waves caused by the textile fabric layers limited its application to only detect the first layer of samples. Frequency response tests of composite samples were also conducted to investigate the effect of mid-layer orientation on dynamic responses. The same trend was observed in the finite element modeling results with a clear effect of the fiber orientation defect seen in frequency response function response and higher mode shapes. Moreover, the results of micro computed tomography demonstrate that this technique could definitely detect the orientation of each layer; however, X-ray imaging at small scales introduced some challenges. Images obtained from ultraviolet technique did not reveal mid-layer orientation. Originality/value – In this paper, the application of different NDT techniques along with finite element modeling to inspect two-dimensional textile composites was presented. Hopefully, the research results presented here will lead to much published papers in inspection of textile composites.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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 | 0.000 | 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 it