Development of an Expert System to Characterize Weld Defects Identified by Ultrasonic Testing
Bibliographic record
Abstract
Welded structures are examined nondestructively, particularly for critical applications where weld failure can be catastrophic, such as in pressure vessels, load-bearing structural members, and power plants. Ultrasonic Testing (UT) is used in the examination of welds in thinner and thicker gauge materials where the size and location of the flaws are important to detect and interpret. Despite the advantages of the ultrasonic technique, the classification of defects based on ultrasonic signals is still frequently questioned, since the analysis and the identification of defect types depend exclusively on the experience and knowledge of the operator. The problem becomes more acute when high inspection rates, high probability of detection, and low number of false results are required. Thus, the correct classification of the type of flaw present in the material reduces measurement errors, increasing the confidence in the test and consequently the safety of the welded structure during service. In the present study, a new algorithm that allows for the detection and measurement of the length and type of weld defects is proposed. The system is based on a coupled dynamic and static patterns in an A-Scan and uses the defects cited in DIN EN 1713 standard as reference for evaluation. The proposed expert system has been evaluated and validated by examining several specimens containing various types of natural (non-artificial) defects identified in the mentioned standard. The results indicate that, the proposed algorithm has a clear potential in automatic defect detection and presents many advantages to the manual method for defect detection and characterization.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".