Applications of non-linear acoustics for quality control and material characterization
Bibliographic record
Abstract
For several decades, nonlinear acoustic methods have been used for material characterization, quality control, and biomedical diagnostics. This approach is based on a second or higher-order phenomenon. Most nondestructive evaluation tasks employ conventional first-order ultrasonic techniques. Utilizing a nonlinear regime may bring new essential information and improve the characterization of materials with defects or flaws that are challenging to detect using traditional acoustical methods. Such defects inexhaustibly include thin cracks and dislocations through which sound passes without reflection; filled cracks or glue layers with acoustical contact between surfaces, voids, and agglomerations thereof with a dimension less than the wavelength; inclusions with a subtle acoustical difference from surrounding media; and multilayer structures with various boundary conditions between layers. For such cases, defects can be detected, visualized, and evaluated using a nonlinear reflection effect. This effect accompanies a typical sound wave reflection at interfaces between media, producing reflected and refracted waves. In the nonlinear regime, these waves have components with double frequency. The nonlinear properties of both media determine the wave amplitude. The nature of the evaluated medium determines the type and number of parameters that describe the nonlinear properties. These parameters' magnitude and spatial distribution provide valuable information about the material properties and object structure. Inspection instruments that utilize the effect of nonlinear reflection can be effective tools for quality control.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".