Terahertz time-domain spectroscopy for the inspection of dry fibre preforms
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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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 | 0.000 | 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) | 0.001 | 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 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".