Full matrix capture preprocessing for multiview total focusing method imaging in welds
Bibliographic record
Abstract
Ultrasonic inspection of austenitic steel welds remains a complex endeavor despite significant progress in ultrasonic data acquisition and imaging methods including the multiview Total Focusing Method (TFM). However, the optimal TFM view can only be chosen with a priori knowledge of the defect position, size, and type. This selection becomes even more complex when the inspected material is anisotropic. This study aims to develop a method to provide information about the defect by processing the Full Matrix Capture (FMC) data before any imaging reconstruction is performed. The FMC provides a rich input for deep learning models but also results in high-dimensional, computationally demanding datasets. To overcome this issue, strategies for data reduction are compared: the Principal Component Analysis (PCA) and the reduction of the FMC by selecting a limited number of A-scans. The dataset comprises simulated FMCs of two-dimensional cracks inside rectangular isotropic steel blocks. The results suggest that both data reduction methods are relevant and that it is possible to provide useful input to the selection of the optimal TFM view. In the next step, the inspection scenario will be further complicated by adding other types of defects and introducing an anisotropic steel weld.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".