Digital Detector Array for Non-destrucitve Radiographic Imaging of Aircraft Structures
Bibliographic record
Abstract
For the safety and airworthiness of aircraft, the aerospace industry has stringent product quality requirements to ensure structural integrity of critical components. Non-destructive inspections (NDI) are routinely performed to ensure product quality and identify defects before they reach critical size. Radiographic inspection plays a key role for inspection of aircraft structural components. Film-based radiography requires consumables, darkroom facility, and manual processing; this is not only time consuming, but also requires more radiation exposure than digital systems. Digital radiography eliminates these requirements and currently in a transition state of switching to two kinds of digital technologies: (1) Digital Detector Arrays, DDA, also known as flat panel detectors, digital radiography, DR, and (2) Computed Radiography, (CR). Flat panel detector (DDA) based allows faster/easier straight acquisition of the radiographic image digitally without the necessity of films or even phosphorous plate like in CR. DR/DDA is also suitable for real time imaging and automation. Before implementing this technology as aircraft inspection procedures, a detailed performance assessment leading to inspection qualification is required. This paper highlights the initial and periodic performance evaluation metrics necessary during initial performance evaluation and periodic maintenance.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
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".