Acoustic emission detection and modal decomposition using a relaxor ferroelectric single crystal linear array
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
• Relaxor Ferroelectric Single Crystal (RFSC) Linear Array for Modal Decomposition and Analysis (LAMDA) design leverages RFSC’s exceptional sensitivity to detect microjoule energy acoustic emission events. • Performs modal decomposition and identifies guided wave modes of a 6.6μJ pencil lead break over a broad bandwidth up to 1.4 MHz. • RFSC-LAMDA outperforms laser vibrometry and wideband AE sensors, demonstrating superior sensitivity and detection for field applications. This paper reports on an acoustic emission (AE) sensor based on relaxor ferroelectric single crystal (RFSC) transduction. The sensor crystal is arranged into a Linear Array for Modal Decomposition and Analysis (LAMDA), with the sensor interrogated by a bespoke high-bandwidth instrument. The efficacy of RFSC LAMDA sensors is showcased through a series of comparative experiments, which include the simultaneous acquisition of pencil lead break (PLB) AEs in a 1.6 mm thick aluminium plate using RFSC LAMDA, a wideband commercial sensor, and laser vibrometry. Subsequent modal decomposition and analysis of the PLB AE signals, as detected by RFSC LAMDA, identified the guided wave modes below 1.4 MHz. Furthermore, it was found that RFSC LAMDA exhibits, on average, 26.6 times greater improvement in sensitivity compared with polyvinylidene fluoride LAMDA variant with near-identical geometry.
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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