Crack detection on composite and metallic aerospace structures
Bibliographic record
Abstract
The Institute for Aerospace Research at the National Research Council Canada (NRC-IAR) has conducted a feasibility study on crack detection sensors for aerospace structures. As part of the feasibility study a composite specimen, modeled after ASTM E1922, was used to scrutinize several possible sensor types. Regular crack gauges, strain gauges and Integral Strain Gauges (ISG) were among the sensors considered in the study. The specimens were subjected to static and fatigue loading cases to initiate the propagation of cracks. Each candidate sensor was then analyzed on its ability to detect and quantify the extent of the damage. In addition, NRC-IAR has also developed a crack detection sensor called Surface Mountable Crack Detection Sensor (SMCS). This system consists of a three-layer insulating and conductive paint system. The area of concern is prepared in the same manner that would be used for the placement of a strain gauge. Due to the nature of the sensor, the shape and geometry is customizable to fit the needs of the region of concern. The conductive nature of the SMCS allows for the system to be interrogated using low voltage signals and minimal power. A wired interrogator that questions the integrity of the sensor has been developed with the intent of providing ease of use for an operator in the field. The experimental and finite element results were investigated to better help us understand the results from our various sensor types. Laboratory testing of composite and metallic coupons with sensors under fatigue loading has been evaluated. An installation kit for the SMCS that allows for the implementation and transfer of this technology to interested organizations has been developed. NRC-IAR is currently testing the SMCS sensors for post-flight inspection of cracked regions in aerospace structures as a means of identifying possible crack growth on the structure.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".