Advances in Automated Non-Destructive Testing for Aircraft Engine Components
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
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.
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 | 0.003 |
| 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 | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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