Experimental Model of Impact Damage for Guided Wave-Based Inspection of Composites
Bibliographic record
Abstract
The objective of this work is to assess to which extent the interaction of antisymmetric ultrasonic guided waves with impact damage can be captured with an experimental model consisting of a single artificial delamination in composite structures. The structures of interest are composed of unidirectional prepreg carbon fiber-reinforced polymer (CFRP) with a quasi-isotropic layup. The artificial delamination is introduced into the laminate using two circular Teflon tapes during manufacturing and the realistic damage is simulated by impacting the samples at two energy levels. Two colocalized rectangular piezoceramics are used to generate an antisymmetric mode and noncontact measurement is performed using a three-dimensional (3D) laser Doppler vibrometer (3D-LDV) to extract the required information for evaluation of the reflection, transmission, as well as the scattering behavior of the antisymmetric mode. The corresponding coefficients as a function of frequency, incident angle, and type of damage are extracted. It is found that the amplitude of the coefficients and directivity patterns of scattered waves are barely affected by incident angle but significantly by the impact energy. In light of the results, design guidelines are proposed for efficient guided wave inspection of composite structures submitted to impacts.
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.
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 | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".