Probe Standoff Optimization Method for Phased Array Ultrasonic TFM Imaging of Curved Parts
Bibliographic record
Abstract
The reliability of the ultrasonic phased array total focusing method (TFM) imaging of parts with curved geometries depends on many factors, one being the probe standoff. Strong artifacts and resolution loss are introduced by some surface profile and standoff combinations, making it impossible to identify defects. This paper, therefore, introduces a probe standoff optimization method (PSOM) to mitigate such effects. Based on a point spread function analysis, the PSOM algorithm finds the standoff with the lowest main lobe width and side lobe level values. Validation experiments were conducted and the TFM imaging performance compared with the PSOM predictions. The experiments consisted of the inspection of concave and convex parts with amplitudes of 0, 5 and 15 λAl, at 12 standoffs varying from 20 to 130 mm. Three internal side-drilled holes at different depths were used as targets. To investigate how the optimal probe standoff improves the TFM, two metrics were used: the signal-to-artifact ratio (SAR) and the array performance indicator (API). The PSF characteristics predicted by the PSOM agreed with the quality of TFM images. A considerable TFM improvement was demonstrated at the optimal standoff calculated by the PSOM. The API of a convex specimen’s TFM was minimized, and the SAR gained up to 13 dB, while the image of a concave specimen gained up to 33 dB in SAR.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".