Assessment of PZT transducer bonding techniques under drop-weight impact loading in composites
Bibliographic record
Abstract
This paper describes the robustness of a structural health monitoring system (SHM) that utilizes lead-zirconatetitanate (PZT) transducers tested on carbon fibre composite coupons under drop-weight impact loading. Four PZT transducers are attached to the surface of 10.16 cm x 15.24 cm aerospace grade carbon fibre coupons using four types of adhesives: cyanoacrylate, epoxy, methyl methacrylate, and silicon. Each PZT transducer is tuned to excite preferentially an A0 mode guided wave burst into each composite coupon prior to and following an impact. The output from a PZT transducer, the amplitude of the propagating guided waves measured using a laser vibrometer on the coupon surface and the RMS velocity is plotted. The cycle is repeated for the three remaining transducers. The electrical admittance is also measured using an impedance analyzer prior to and following impact. This paper illustrates how a robustness metric expressed in terms of admittance can be used to infer the ability of the SHM system to generate guided waves and to detect damage following impact. The robustness metric is a measure of the adhesive strength and the mechanism to provide accurate damage detection results. It is shown that transducers attached using silicon provide accurate damage detection results based on pre-attached adhesive yielding difference of <0.5% obtained from electrical admittance measurements before and after impact.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".