Demonstration of an instrumented patch
Bibliographic record
Abstract
The primary objective of this study was to demonstrate the effectiveness of various strain measurement techniques at detecting the disbonding of a composite repair patch and then using this information to validate a new capacitance based disbond detection technique. The instrumented repair patch was parametrically designed with the help of Finite Element Analysis (FEA) software to have a stress concentration at its tip. This stress concentration was designed to produce a disbond during fatigue testing, without the need for the introduction of any foreign material to create an artificial disbond condition. The aluminum substrate was grit blasted and the instrumented patch was bonded using FM<sup>®</sup>73 adhesive, and was cured following the recommendations of the manufacturer. The geometric characteristics of the patch followed standard repair guidelines for such variables as material selection, taper angles and loading conditions, with the exception of the area designed for premature disbond. All test specimens were inspected using non-destructive testing technique (ultrasound pulse echo) to guarantee that no disbonding had occurred during curing of the specimen. The specimens were placed under fatigue loading to induce a disbond condition between the aluminum substrate and the patch. The specimens were cyclically loaded and strain gauges bonded to strategic locations on the aluminum and composite patch surface to be able to measure changes in surface strains as the disbond progressed. A Digital Image Correlation (DIC) system was also used to measure full field strains over the gauge length of the coupon. The DIC results were compared with the strain gauge data and were used to provide a qualitative measure of the load transfer in the bonded specimen, which clearly demonstrated the change in surface strain that occurred as the composite patch disbonded from the aluminum substrate. Thermoelastic Stress Analysis (TSA) was also used to measure surface strains on the composite patch. Thermoelastic stress analysis proved to be the most sensitive technique for experimentally monitoring the disbond process in real time. Failure analysis of the specimens using optical microscope techniques was performed to determine the type of failure between the patch and the substrate. The results of this work will serve to test the different types of sensors available for the design and manufacturing of a "Smart Patch" for aircraft structure applications.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".