Prognostics of damage growth in composite materials using machine learning techniques
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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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".