SmartComposite system for impact damage detection on composite structures
Bibliographic record
Abstract
Composites are increasingly used in numerous structural applications because of their low weight-to-strength and weight-to-stiffness ratios. However, the performance and behavior characteristics of nearly all in-service composite structures can be affected by degradation resulting from sustained use as well as from exposure to severe environmental conditions or damage resulting from external conditions such as impact, loading abrasion, operator abuse. These factors can have serious consequences on the structures relative to safety, cost, and operational capability. In this paper, a SmartComposite system is introduced for monitoring the integrity of large composite structures. Key features of the system include miniaturized lightweight hardware, self-diagnostics and an adaptive algorithm to automatically compensate for damaged sensors, reliable damage detection under different environmental conditions, and generation of POD curves. Tests were conducted on composite test article with sensor network embedded inside the composite skin or surface mounted to demonstrate the impact damage detection capability of the SmartComposite System. It is clear from the test results that the SmartComposite system can successfully detect impact damages, including both damage location and probability of damage size.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 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".