Lamb wave-based experimental and numerical studies for detection and sizing of corrosion damage in metallic plates
Bibliographic record
Abstract
Lamb waves are ultrasonic-guided waves with applications in inspection and monitoring of plate-like structures. These waves can be used for detecting, locating, and sizing of defects. In this paper, a new method is proposed for in situ measurement of the location and size of circle-like corrosion defects in thin plates. A novel technique for omnidirectional generation of Lamb waves is also proposed. The probe is placed on at least three different points around the defect and the arrival times of reflected echoes are measured. An algorithm then estimates the location and size of the defect based on the arrival times of reflected echoes. A finite element model is also developed for modeling the process and studying various aspects of the method. The proposed method is then tested on an aluminum plate. The center location and radius of a 5-mm hole in a 0.5-mm thick aluminum plate is estimated with uncertainties of ±1 mm (1%) and ±0.25 mm (5%), respectively. Various aspects of the proposed method are discussed, and uncertainties in measurements are estimated. Effectiveness of the proposed method is also assessed by sizing actual corrosion defects. The proposed method is fast, flexible, and portable and shows better accuracy in comparison to similar existing methods.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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".