Prognostics of damage growth in composite materials using machine learning techniques
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
Composite materials have been adopted and become critical in aerospace industry. However, due to the fatigue under continuous loading, the uncertain in structural integrity still remains an unsolved problem. The assessment of structural damage in composite laminates can be achieved by damage location, classification, and quantification. The growth trend of delamination area is one of the most important factors. In order to predict the delamination size efficiently and accurately, this paper proposes a prognostic method based on machine learning techniques. Prediction models, including linear model, support vector machines, and random forests were investigated. An optimal solution was identified by comparing the test results of different models. In this study, the length of the path across delamination area was selected as the objective value to train the models. The path length measurements augmented the training data sets and avoid the overfitting problem for the models. Moreover, the path length can be used to measure the size of delamination area. The interrogation frequency collected on several composite coupons was adopted as an input variable for the predict model. Experimental results demonstrate the effectiveness of the proposed method.
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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 | 0.000 |
| 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 it