U-NET autoencoder for amplitude recovery on binarized Full Matrix Captures
Bibliographic record
Abstract
Full Matrix Capture (FMC) and the Total Focusing Method (TFM) are instrumental techniques in ultrasonic nondestructive testing (NDT) in industries such as aerospace, oil and gas, and manufacturing, and allow efficient defect detection by capturing all possible transmitter–receiver pairs and generating highly resolved images on a predefined pixel grid. The use of dense linear or matrix probes presents significant challenges in data storage and transfer but also in the complexity of the acquisition system’s electronics. In this context, binary acquisition steps in as an attractive alternative for simplifying acquisition equipment and reducing data size. However, binary formats carry the drawback of amplitude information loss. To address this, the present study explores the application of a U-NET autoencoder neural network to reconstruct amplitude data from binarized FMC signals. The autoencoder’s U-NET architecture is particularly suited for this task due to its effectiveness with limited datasets, a common issue in NDT. Finite element simulations were used to generate training and validation datasets. Experimental tests were then conducted on steel samples containing various defects, such as Electrical Discharge Machining (EDM) cracks, side-drilled holes (SDH), and a realistic fatigue crack in a steel bar. The reconstructed FMC data were evaluated using TFM images and Structural Similarity Index Measure (SSIM), showing that the neural network accurately reconstructed FMCs. Notwithstanding the presence of minor amplitude errors, the spatial positioning of defects remained precise, demonstrating the method’s viability for practical NDT applications.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".