Advances in Automated Non-Destructive Testing for Aircraft Engine Components
Bibliographic record
Abstract
Non-destructive testing (NDT) is crucial for aero-engine components throughout their lifecycle, from raw material processing to finished product assembly and during maintenance, repair, and overhaul (MRO) operations. Current NDT practices for these components primarily rely on manual methods, including visual inspection, digital X-ray, thermography, ultrasonic testing, and eddy current techniques. However, advancements in engine manufacturing processes, such as laser welding, brazing, and advanced coatings, have resulted in increasingly complex part geometries, posing significant challenges for preand post-repair inspection. Furthermore, the emergence of advanced engine designs incorporating novel composite materials and complex 3D-manufactured turbine blades further raise the challenges in quality control and MRO. Consequently, Automated NDT inspection technologies are becoming essential solutions where manual or conventional methods are impractical or infeasible. Automated systems offer the necessary tools for efficient and reliable inspection of complex components. This article presents illustrative examples of Automated NDT applications for specific engine components, including compressor discs, engine bearings, turbine nozzles, fan blades, and fan cases.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".