Theoretical Assessment of Different Ultrasonic Configurations for Delamination Defects Detection in Composite Components
Bibliographic record
Abstract
It is well known that structures’ safety is crucial and of great importance. Part of their maintenance procedure is structural inspection, which is currently performed with the aid of Non Destructive Testing techniques, aiming to detect structural defects in damaged or flawed components and prevent a catastrophic failure by substituting or repairing them. The objective of this work is the theoretical assessment of different ultrasonic configurations that could maximize delamination defect detection in composite components. Modeling study was performed using simulation software, where physical models representative of laminated Carbon Fiber Reinforced Polymer composites, consisting of a variety of artificial delamination defect modes (different sizes and depth), were numerically tested. Different ultrasonic configurations on both the positioning and the firing of the probe's elements including Phased Array delay timings and sampled array techniques were investigated and are presented in this paper. The potential of Full Matrix Capture data acquisition technique, modelled here, along with the post processing Total Focusing Method reconstruction approach is also assessed in terms of their ability to enhance defect detectability and visualization.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".