Creating schematic representation of corrosion using CGAN and ultrasonic imaging
Bibliographic record
Abstract
Ultrasonic nondestructive testing (NDT) techniques, such as Full Matrix Capture (FMC) combined with the Total Focusing Method (TFM), are widely used for identifying material flaws. However, while these methods can detect defects and corrosion, they fall short in accurately reconstructing their exact shape and position, relying heavily on manual interpretation from a highly trained inspector. This study explores the application of artificial intelligence (AI) to enhance the analysis of FMC/TFM images. By leveraging conditional Generative Adversarial Networks (cGANs), the proposed approach improves the reconstruction of defects such as corrosion, accurately modeling both front and back walls of the test specimen for precise dimensional assessment. Trained on highly corroded simulated data, the cGAN generates detailed 2D representations of corrosion profiles.
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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".