Terahertz time-domain spectroscopy for the inspection of dry fibre preforms
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
Liquid moulded polymer matrix composites (LM-PMCs) are increasingly used in aerospace, automotive and other industrial applications. Liquid moulding processes featuring dry fibre preforms provide flexibility and enable cost reductions for secondary load-bearing structures. However, preform variability and manufacturing reproducibility remain major obstacles to wider use in primary structures. The open literature records only marginal use of non-destructive inspection (NDI) methods for dry multilayer preforms due to technological limitations and cost of NDI methods. In this work, terahertz time-domain spectroscopy (THz-TDS) is used for inspecting three dry multilayer glass fibre preforms featuring different defects, for the first time. A novel time-domain enhancement method is compared with classical image processing methods, aiming at improving image contrast and detecting potential defects. Furthermore, THz B-Scan is used for verifying the accuracy of interply defect detection. Finite difference time domain is simulated for analyzing THz magnitude variation in time-domain. Finally, quantitative evaluation is applied to further illustrate the significant potential of THz-TDS for the inspection of dry fibre preforms. The results show that the errors on defects lengths, widths, angles, and diameters for THz-TDS are in the ranges 8.2% ∼ 34.3%, 18.67% ∼ 75%, 0.29% ∼ 6.67%, and 1.33% ∼ 10% respectively.
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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 | 0.000 | 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