Continuous crack monitoring of metallic structures using carbon nanotube-based epoxy thin films
Bibliographic record
Abstract
This work focuses on the application of epoxy nanocomposite thin film sensors for continuous monitoring of crack evolution in metallic structures. The approach taken was to monitor the current or resistance change in these nanocomposite films as cracks developed and propagated in the metallic host structure. Based on optical, electrical and mechanical properties of epoxy resins modified with different contents of single-walled carbon nanotubes (SWCNT), two different nanocomposites (with 0.3 wt% and 1.0 wt% of SWCNT) were chosen for the development of a crack sensor. The performance of the nanocomposite sensors was evaluated under tension-tension fatigue tests on aluminum coupons with centrally located through thickness electrical discharge machined (EDM) notches. Crack growth in the aluminum was found to transfer to the nanocomposite films in a stable mode. Once the crack was established, a linear correlation was found between the measured current and crack length with a slope of -10 -11 A/mm and -10 -8 A/mm for nanocomposites, with 0.3 wt% and 1.0 wt% of SWCNT, respectively. Contact between the asperities formed on the crack surfaces in the nanocomposite film while the crack was closed was found to be an important limiting factor causing a large variation in measured currents during each fatigue cycle. In summary, the nanocomposite thin film sensor developed in this work offers continuous crack growth monitoring. The sensor is also suitable for visual inspection of the host structure due to the transparency of the developed nanocomposite film.
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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 | 0.000 |
| 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 | 0.000 |
| 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 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".