AI-based Model to Estimate the Flaws from Ultrasonic NDT Data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study explores an artificial intelligence (AI) approach for assessing the geometrical features of flaws in nondestructive testing (NDT) using ultrasonic oscillograms. A validated numerical model, generated through acoustic finite element analysis (FEA), produced ultrasonic signals. The AI model was trained on 525, validated on 113, and tested on 112 oscillograms from models with varied flaw characteristics. Training inputs were parameters derived from ultrasonic signals, and the network's performance was evaluated by comparing its outputs for flaw location, length, and angle with desired values. Statistical analysis, including Root Mean Square Error (RMSE) and Efficiency (E), indicated promising results, suggesting the potential of the proposed AI-based method for estimating flaw geometrical features in ultrasonic NDT.
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.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it